Multi-scale analysis of the relationship between landscape patterns and a water quality index (WQI) based on a stepwise linear regression (SLR) and geographically weighted regression (GWR) in the Ebinur Lake oasis

Estimating thresholds of natural vegetation for the protection of water quality in South African catchments

Many of South Africa's current water quality problems have been attributed to diffuse pollution derived from poorly regulated land use/land cover (LULC) transformations. To mitigate these impacts, the preservation of an adequate amount of natural vegetation within catchment areas is an important management strategy. However, it is not clear how much natural vegetation cover is required to provide adequate levels of protection, nor at which scale(s) this strategy would be most effective. To investigate the possibility of estimating minimum thresholds of natural vegetation required to protect water resources, regression analysis was used to model relationships between water quality (measured using Nemerow's Pollution Index) and metrics of natural vegetation at multiple scales across a sample of sub-catchments located along the western, southern, and south-eastern coast of South Africa. With conspicuous outliers removed, the models were able to explain up to 82 % of the variability in the relationship between land use and water quality. Moreover, a statistically significant, nonlinear, and inverse relationship was found between proportions of natural vegetation cover and pollution levels. This relationship was strongest when measured (1) across the whole catchment and (2) within a 200 m riparian buffer zone. The models further indicated that approximately 80 to 90 % natural vegetation cover was necessary at these scales to maintain water quality at ecologically acceptable levels. Additional nonlinear thresholds estimated using breakpoint analysis suggested that if proportions of natural vegetation fall below 45 % (across the whole catchment) and 60 % (within a 200 m riparian buffer zone) a dramatic increase in pollution levels can be expected. The estimated thresholds are recommended as guidelines that can be used to inform integrated land and water resources management strategies aimed at protecting water quality in the study area. Likewise, the methods described are recommended for the estimation of similar thresholds in other regions.

Insight into soft chemometric computational learning for modelling oily-wastewater separation efficiency and permeate flux of polypyrrole-decorated ceramic-polymeric membranes

Reliable modeling of oily wastewater emphasizes the paramount importance of sustainable and health-conscious wastewater management practices, which directly aligns with the Sustainable Development Goals (SDG) while also meeting the guidelines of the World Health Organization (WHO). This research explores the efficiency of utilizing polypyrrole-coated ceramic-polymeric membranes to model oily wastewater separation efficiency (SE) and permeate flux (PF) based on established experimental procedures. In this area, computational simulation still needs to be explored. The study developed predictive regression models, including robust linear regression (RLR), stepwise linear regression (SWR) and linear regression (LR) for the ceramic-polymeric porous membrane, aiming to interpret its complex performance across diverse conditions and, thus, develop its utility in oily wastewater treatment applications. Subsequently, a novel, simple average ensemble paradigm was explored to reduce errors and improve prediction skills. Prior to the development of the model, stability and reliability analysis of the data was conducted based on Philip Perron tests with the Bartlett kernel estimation method. The accuracy of the SE exhibited a high consistency, averaging 99.92% with minimal variability (standard deviation of 0.026%), potentially simplifying its prediction compared to PF. The modes were validated and evaluated using metrics like MAE, RMSE, Speed, and MSE, in addition to 2D graphical and cumulative distribution function graphs. The LR model emerged as the best with the lowest RMSE =0.21951, indicating superior prediction accuracy, followed closely by RLR with an RMSE = 0.22359. SWLR, while having the highest RMSE = 0.34573, marked its dominance in prediction speed with 110 observations per second. Notably, the RLR model justified a reduction in error by approximately 35.29% compared to SWLR. Moreover, the training efficiency of the LR model exceeded, demanding a mere 2.9252 s, marking a reduction of about 32.54% compared to SWLR. The improved simple ensemble learning proved merit over the three models regarding error accuracy. This study emphasizes the essential role of soft-computing learning in optimizing the design and performance of ceramic-polymeric membranes.

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.

Landsat observations of total suspended solids concentrations in the Pearl River Estuary, China, over the past 36 years

Information on long-term trends in total suspended solids (TSS) is critical for assessing aquatic ecosystems. However, the long-term patterns of TSS concentration (CTSS) and its latent drivers have not been well investigated. In this study, we developed and validated three semi-analysis algorithms for deriving CTSS using Landsat images. Subsequently, the long-term trends in CTSS in the Pearl River Estuary (PRE) from 1987 to 2022 and the driving factors were clarified. The developed algorithms yielded excellent performance in estimating CTSS, with mean absolute percentage errors <25% and root mean square errors of <13 mg/L. Long-term Landsat observations showed an overall decreasing trend and significant spatiotemporal dynamics of the CTSS in the PRE from 1987 to 2022. The analysis of driving factors suggested that industrial sewage, cropland, forests and grasslands, and built-up land were the four potential driving forces that explained 87.81% of the long-term variation in CTSS. This study not only provides 36-year recorded datasets of CTSS in estuary water, but also offers new insights into the complex mechanisms that regulate CTSS spatiotemporal dynamics for water resource management.

Assessing the impact of urbanization on groundwater quality of lahore region, Pakistan

Land use land cover (LULC) dynamics is an important aspect of environmental studies. Lahore is one of the wide-ranging urban cities in the world experiencing rapid development in the form of unplanned urban growth and industrialization, which leads to many adverse consequences. This research focuses on the study of spatio-temporal variability of urbanization and its impact on the water quality index (WQI) in Lahore city using remote sensing (RS) and geographical information systems (GIS). Landsat images (Landsat 7 ETM+, Landsat 8 OLI) between 2005 to 2021 were used to observe the changes in urban growth over seventeen years. GIS is used to create the LULC, normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI) maps, to study the urbanization impact on the WQI. The results of this study indicate that the groundwater quality of metropolitan Lahore city has significantly dropped within 17 years. The extent of the built-up area has been expanded from 22.4% to 953.04% with an increase in the poor WQI area from 1.95% to 37.89%, reveals a general decline in groundwater quality with urbanization. Indeed, the trends observed by the linear regression modelling showed a positive and negative correlation (R2 = 0.67 and -0.74) of WQI with % of urban and vegetation areas respectively. GIS and RS tools have been found effective in assessing spatio-temporal phenomena of urbanization and its impact on groundwater quality. Furthermore, this research would be very helpful in making decisions for managing groundwater resources and illegal urban expansion in Lahore city.

Long-term dynamics and drivers of particulate phosphorus concentration in eutrophic lake Chaohu, China

Particulate phosphorus (PP) plays an important biological role in the eutrophication process, and is thus an important water quality parameter for assessing climatic change and anthropogenic activity factors that affect aquatic ecosystems. Here, we used 20-year Moderate Resolution Imaging Spectroradiometer (MODIS) data to explore the patterns and trends of PP concentration (C_PP) in eutrophic Lake Chaohu based on a new empirical model. The validation results indicated that the developed model performed satisfactorily in estimating C_PP, with a mean absolute percentage error of 31.89% and root mean square error of 0.022 mg/L. Long-term MODIS observations (2000-2019) revealed that the C_PP of Lake Chaohu has experienced an overall increasing trend and distinct spatiotemporal heterogeneity. The driving factor analysis revealed that the chemical fertilizer consumption, municipal wastewater, industrial sewage, precipitation, and air temperature were the five potential driving factors and collectively explained more than 81% of the long-term variation in C_PP. This study provides the long-term datasets of C_PP in inland waters and new insights for future water eutrophication control and restoration efforts.

Habitat quality assessment of wintering migratory birds in Poyang Lake National Nature Reserve based on InVEST model

Poyang Lake National Nature Reserve (PLNNR) is an important resting place for wintering migratory birds on the East Asian-Australasian Flyway (EAAF). In recent years, due to human activities and climate change, the area of wetlands has shown a downward trend, and the number and habitat of wintering migratory birds have been threatened. It is urgent to evaluate the habitat quality of wintering migratory birds in PLNNR. Therefore, the InVEST model and landscape index were used to evaluate the habitat quality of wintering migratory birds, and the grey correlation theory was used to reveal the response of typical wintering migratory bird population to habitat quality. The results showed that the habitat quality of the PLNNR was still at a high level, but showed a downward trend, with the average index of habitat quality decreasing from 0.872 to 0.817. The area of the highest quality habitat decreased by 3394.92 hm², the area of the lowest, low, and medium quality habitats increased by 3112.11 hm², and the area of the high quality habitat remained stable. The lowest, low, and medium quality habitat expanded from the middle to the south of the PLNNR mainly because of the expansion of construction land and cultivated land. The area with deterioration in habitat quality was 10,477.53 hm², mainly concentrated in the center and south of the PLNNR. The area with restoration in habitat quality was 6148.26 hm², mainly concentrated in the Bang Lake and Dacha Lake. The area with no change in habitat quality remained stable. The fragmentation degree and shape complexity of highest and high quality habitats increased, dominance degree and connectivity decreased, and the landscape pattern of habitat quality showed a downward trend. Typical wintering migratory birds have a strong correlation with highest, high, and low habitat quality, and there is a downward trend with the deterioration of habitat quality. Finally, this paper puts forward constructive suggestions on the degradation of habitat quality caused by land-use change.

Evaluation of the water quality of a highly polluted stream with water quality indices and health risk assessment methods

The water quality of Çorlu Stream, located in the Thrace region of Türkiye, and exposed to intense industrial pressure, was evaluated by monitoring 10 toxic metals and 13 other water quality variables in the dry and wet seasons of 2021. Seven different water quality indices were applied to determine the pollution level at the sampling stations in the stream. In addition, human health risks from exposure to toxic metals in stream water via ingestion and dermal contact were evaluated. The results showed that the water quality at stations S2 and S3 of Çorlu Stream receiving domestic and industrial discharges are seriously polluted by NH4-N, PO4-P, COD, BOD5 and suspended solids according to surface water quality standards. In addition, these stations were highly polluted and had poor water quality according to the results of the water quality indices. The average Cr level at station S3 exceeded the permissible levels set for the protection of aquatic life due to effluent discharges from the leather factories. Considering the results of the health risk assessment methods, non-carcinogenic risks from ingestion of combined metals in stream water can be expected at station S3 for both children and adults and at station S2 for children. Also, it was estimated that Cr and As at station S3 may cause carcinogenic health risks for residents.

Adaptive-weight water quality assessment and human health risk analysis for river water in Hong Kong

The water quality of Hong Kong's four water control zones (Tolo Harbour and Channel, Port Shelter, Victoria Harbour, and Junk Bay) is of vital importance and has attracted much attention. This study aims to more objectively and comprehensively assess the water quality and its health impact based on the four-year monitoring data of 21 parameters collected from four zones. First, physicochemical characteristics of the water system were investigated based on multivariate statistical approaches, including Kruskal-Wallis test, hierarchical cluster analysis, and Mann-Kendall test. Then, water quality levels over space and time and the element sources were analyzed using adaptive-weight water quality index (AWQI) method, and factor analysis, respectively. Finally, the potential harm of trace elements for humankind was identified based on the health risk assessment model. The results revealed that (1) the values of more than half of the water quality parameters exhibited significant interannual changes, and the values of all parameters distinctly varied over space; (2) The water quality status in four water control zones showed a steady and long-term improvement trend from 2016 to 2019; (3) The sources of pollution elements impacting water quality status were related to the comprehensive influence of human activities and natural processes; (4) The carcinogenic risks of all trace elements were negligible or acceptable, while Mn and As may cause noncarcinogenic harm to humankind.

Analysis of Pathogen Characteristics and Nursing Factors of Tonsil Infection Based on Regression Equation

In order to meet the needs of the analysis and application of regression equation in clinical medicine of tonsil infection, this paper focuses on the semiparametric regression model method, cross-validation method, empirical method, and multiple regression equation analysis of atypical data using regression equation. The general method of analyzing this kind of data is given, and the parameter estimation and hypothesis testing of the model are systematically studied. The experimental results showed that among the 90 paraffin-embedded tissue specimens of chronic tonsillitis and adenoid hypertrophy in this study, 26 out of 49 male children were EBERs positive, accounting for 53.06% of male children (26/49 cases). 28 of the 41 female children were positive, accounting for 68.29 of the female children (28/41 cases). There were 14 cases in infant group, 20 cases in preschool age group, 25 cases in school-age group, and 31 cases in adolescence group; the EBERs-positive rate was 42.86% (6/14 cases) in early childhood and 55.00% in early school-age (11/20 cases), and the EBERs-positive rate was 60.00% in school-age group (15/20 cases) and 70.97% in adolescent group. The results showed that the latent infection rate of adenoid hypertrophy EBV in children with chronic tonsillitis showed no significant difference between genders. It is proved that the regression equation method can meet the needs of clinical analysis and application of tonsil infection.

Spatial and physicochemical assessment of groundwater quality in the urban coastal region of Sri Lanka

Rising sea levels, groundwater exploitation, and urbanization were the primary causes of seawater intrusion, exerting pressure on coastal aquifers. In Sri Lanka's urban coastal region, a comprehensive physicochemical description of groundwater has yet to be identified. Therefore, the objectives of this research were to (a) use a Geographic Information System (GIS) to designate spatial distribution of various water physicochemical characteristics, (b) detect "suitable" groundwater zones for drinking, and (c) estimate groundwater quality by developing a groundwater quality index (GWQI) in Sri Lanka's urban coastal region. The physiochemical parameters of 18 groundwater samples [pH, electrical conductivity (EC), turbidity, total dissolved solids (TDS), Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl^-, and HCO₃^-] were studied in terms of their spatial and temporal variation. According to the World Health Organization (WHO) and Sri Lankan Standard Institution (SLS), EC levels in 11% of samples were above the acceptable range, while turbidity levels in 22% of samples were above the acceptable range. Water was consumable in 77.78% of the locations and unsatisfactory in 22.22%. The main hydrochemical facies detected in groundwater samples were Na⁺- Cl^- and the mixed Ca²⁺- Mg²⁺- Cl^- face, which indicated carbonate dissolution and weathering of silicate minerals and the main mechanism controlling the water chemistry in the study area is water-rock interaction. Based on daily water consumption, it was discovered that the HQ is greater than one, in 61% of males, 78% of females, and 89% of children, indicating a health hazard. Furthermore, groundwater quality in the study region is deteriorating due to significant coastal erosion, making it critical to maintain a comprehensive groundwater management strategy to promote sustainable water consumption.

Assessment of physicochemical parameters of Vishav stream: an important tributary of river Jhelum, Kashmir Himalaya, India

The deteriorating conditions in stream ecosystems are detrimental for society as far as its health, and development is concerned if the underlying factors continue to operate without regular monitoring. In order to maintain the health of a stream ecosystem, assessment of spatiotemporal changes in its physicochemical attributes and identification of all factors that could alter its hydrological regime is an essential component for managing it. The current 2-year study (October-2017 to September-2019) assessed the physicochemical regime of lower stretches of the Vishav stream, a major left-bank tributary of river Jhelum on a spatiotemporal basis. The physicochemical data was analyzed through linear regression, ANOVA (followed by Duncan's test), multivariate statistical analysis, viz., principal component analysis (PCA) and cluster analysis (CA). Linear regression pointed out the nature and magnitude of the relationship between different physicochemical variables (p < 0.05). PCA showed that WT, pH, EC, NO₃-N, TDS, TH, and DO are the major factors reflecting the water quality of the Vishav stream. The range in water quality parameters of the Vishav stream was found conducive for the inhabitant fishes. Two well-defined clusters were obtained, wherein Cluster-I comprising of Site-III (a downstream pollution prone site) and Cluster-II comprising of Site-II and Site-I (mid- and upstream site respectively) are less prone to human interferences. The present study could serve as baseline information to manage and conserve this precious element of the aquatic ecosphere in terms of better water quality for humans and its inhabitant faunal elements especially fish which play a significant role in the economy of that region.

Assessment of extrinsic and intrinsic influences on water quality variation in subtropical agricultural multipond systems

Understanding wetland water quality dynamics and associated influencing factors is important to assess the numerous ecosystem services they provide. We present a combined self-organizing map (SOM) and linear mixed-effects model (LMEM) to relate water quality variation of multipond systems (MPSs, a common type of non-floodplain wetlands in agricultural regions of southern China) to their extrinsic and intrinsic influences for the first time. Across the 6 test MPSs with environmental gradients, ammonium nitrogen (NH₄⁺-N), total nitrogen (TN), and total phosphate (TP) almost always exceeded the surface water quality standard (2.0, 2.0, and 0.4 mg/L, respectively) in the up- and midstream ponds, while chlorophyll-a (Chl-a) exhibited hypertrophic state (≥28 μg/L) in the midstream ponds during the wet season. Synergistic influences explained 69±12% and 73±10% of the water quality variations in the wet and dry season, respectively. The adverse, extrinsic influences were generally 1.4, 6.9, 3.2, and 4.3 times of the beneficial, intrinsic influences for NH₄⁺-N, nitrate nitrogen (NO₃^--N), TP, and potassium permanganate index (COD_Mn), respectively, although the influencing direction and degree of forest and water area proportion were spatiotemporally unstable. While COD_Mn was primarily linked with rural residential areas in the midstream, higher TN and TP concentrations in the up- and midstream were associated with agricultural land, and NH₄⁺-N reflected a small but non-negligible source of free-range poultry feeding. Pond surface sediments exhibited consistent, adverse effects with amplifications during rainfall, while macrophyte biomass can reflect the biological uptake of COD_Mn and Chl-a, especially in the mid- and downstream during the wet season. Our study advances nonpoint source pollution (NPSP) research for small water bodies, explores nutrient "source-sink" dynamics, and provides a timely guide for rural planning and pond management. The modelling procedures and analytical results can inform refined assessment of similar NFWs elsewhere, where restoration efforts are required.

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.

A simulation-based method to develop strategies for nitrogen pollution control in a creek watershed with sparse data

Well-defined targets for nitrogen (N) release into the local environment are essential for water management in creeks, but difficulties often arise from working with data that are too sparse to achieve reliable evaluations. Here, a simulation-optimization approach based on the QUAL2K model was developed to put forward strategies for nitrogen pollution control in a creek with sparse data in Shixi Creek, southeast China. The model showed good agreement with field observations from 22 sampling sites sampled over the period from March 2017 to February 2019, with normalized objective function (NOF) less than 0.360. Based on this model, the water pollutant sources in the creek were distinguished and analyzed. Rural sewage discharge in Shixi Creek was the major factor threatening water quality in the stream. Seasonal variations may influence the transformation of riverine N. To make more than 80% of the area in Shixi Creek meet the water quality standard of grade III, an optimized approach is to reduce more than 55% of the N pollution from point source pollution and 10% from nonpoint source pollution. This study proposed an approach that can effectively evaluate strategies for water management in a creek watershed with sparse data.

Use of water quality index and multivariate statistical methods for the evaluation of water quality of a stream affected by multiple stressors: A case study

The Sürgü Stream, located in the Euphrates River basin of Turkey, is used for drinking water source, agricultural irrigation and rainbow trout production. Therefore, water quality of the stream is of great importance. In this study, multivariate statistical techniques (MSTs) and water quality index (WQI) were applied to assess water quality of the stream affected by multiple stressors such as untreated domestic sewage, effluents from fish farms, agricultural runoff and streambank erosion. For this, 16 water quality parameters at five sites along the stream were monitored monthly during one year. Most of parameters showed significant spatial variations, indicating the influence of anthropogenic activities. All parameters except TN (total nitrogen) showed significant seasonal differences due to high seasonality in WT (water temperature) and water flow. The spatial variations in the WQI were significant (p < 0.05) and the mean WQI values ranged from 87.6 to 95.3, indicating "good" to "excellent" water quality in the stream. Cluster analysis classified five sites into three groups, that is, clean region, low polluted region and very clean region. Stepwise temporal discriminant analysis (DA) identified that pH, WT, Cl^-, SO₄^2-, COD (chemical oxygen demand), TSS (total suspended solids) and Ca²⁺ are the parameters responsible for variations between seasons, and stepwise spatial DA identified that DO (dissolved oxygen), EC (electrical conductivity), NH₄-N, TN (total nitrogen) and TSS are the parameters responsible for variations between the regions. Principal component analysis/factor analysis revealed that the parameters responsible for water quality variations were mainly associated with suspended solids (both natural and anthropogenic), soluble salts (natural) and nutrients and organic matter (anthropogenic).

Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index

In recent decades, various conventional techniques have been formulated around the world to evaluate the overall water quality (WQ) at particular locations. In the present study, back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and one multilinear regression (MLR) are considered for the prediction of water quality index (WQI) at three stations, namely Nizamuddin, Palla, and Udi (Chambal), across the Yamuna River, India. The nonlinear ensemble technique was proposed using the neural network ensemble (NNE) approach to improve the performance accuracy of the single models. The observed WQ parameters were provided by the Central Pollution Control Board (CPCB) including dissolved oxygen (DO), pH, biological oxygen demand (BOD), ammonia (NH₃), temperature (T), and WQI. The performance of the models was evaluated by various statistical indices. The obtained results indicated the feasibility of the developed data intelligence models for predicting the WQI at the three stations with the superior modelling results of the NNE. The results also showed that the minimum values for root mean square (RMS) varied between 0.1213 and 0.4107, 0.003 and 0.0367, and 0.002 and 0.0272 for Nizamuddin, Palla, and Udi (Chambal), respectively. ANFIS-M3, BPNN-M4, and BPNN-M3 improved the performance with regard to an absolute error by 41%, 4%, and 3%, over other models for Nizamuddin, Palla, and Udi (Chambal) stations, respectively. The predictive comparison demonstrated that NNE proved to be effective and can therefore serve as a reliable prediction approach. The inferences of this paper would be of interest to policymakers in terms of WQ for establishing sustainable management strategies of water resources.

Mapping spatio-temporal dynamics of main water parameters and understanding their relationships with driving factors using GF-1 images in a clear reservoir

Due to eutrophication and water quality deterioration in clear reservoirs, it is necessary to monitor and manage the main water parameters: concentration of total phosphorus (CTP), chemical oxygen demand (CCOD), chlorophyll-a (CChla), total suspended matter (CTSM), and Secchi disk depth (SDD). Five random forest (RF) models are developed to estimate these parameters in Xin'anjiang Reservoir, which is a clear drinking water resource in Zhejiang, China. Then, the spatio-temporal distributions of the parameters over 7 years (2013-2019) are mapped using GaoFen-1 (GF-1) images and the relationships with driving factors are analyzed. Our study demonstrates that the parameters' distributions exhibited a significant spatio-temporal difference in Xin'anjiang Reservoir. Spatially, relatively high CTP, CCOD, CChla, and CTSM but low SDD appear in riverine areas, showing strong evidence of impact from the incoming rivers. Temporally, CChla and CTSM reached high values in summer and winter, whereas SDD and CTP were higher in the summer and autumn, respectively. In contrast, no significant seasonal variations of CCOD could be observed. This may be why CCOD is not sensitive to hydrological or meteorological factors. However, precipitation had a significant impact on CChla, CTP, SDD, and CTSM in riverine areas, though these parameters were less sensitive to meteorological factors. Moreover, the geomorphology of the reservoir and anthropogenic interference (e.g., tourism activities) also have a significant impact on the water quality parameters. This study demonstrates that coupling long-term GF-1 images and RF models could provide strong evidence and new insights to understand long-term dynamics in water quality and therefore support the development of corresponding management strategies for freshwater reservoirs.

Climatic seasonality and water quality in watersheds: a study case in Limoeiro River watershed in the western region of São Paulo State, Brazil

Applying the climatological water balance (WB) concept to describe the relationship between climatic seasonality and surface water quality according to different forms of land use and land cover (LULC) is an important issue, but little explored in the literature. In this paper, we evaluate the influence of WB on surface water quality and its impacts when interacting with LULC. We monitored 11 sampling points during the four seasons of the year, from which we estimate WQI (water quality index) and TSI (trophic state index). We found an effect of the seasonality factor on both WQI values (F(3,30) = 12.472; p < 0.01) and in TSI values (F(3,30) = 6.967; p < 0.01). We noticed that LULC interferes in the way that the water balance influences the WQI and TSI values since in sampling points closest to higher urban density, with little or no riparian protection, the correlation between water balance and water quality was lower. In the stations that had the lowest water surplus and deficit, there was positive linearity between water balance and WQI. However, in the seasons when the surplus and water deficit recorded were extreme, there was no linearity. We conclude that water deficiency impairs the quality of surface water. In the extreme surplus water season, the homogeneity of WQI samples was lower, suggesting a higher interaction between rainwater and LULC. This study contributes to design management strategies of water resources, considering the climatic seasonality for optimization.

Response of Water Quality to Landscape Patterns in an Urbanized Watershed in Hangzhou, China

Intense human activities and drastic land use changes in rapidly urbanized areas may cause serious water quality degradation. In this study, we explored the effects of land use on water quality from a landscape perspective. We took a rapidly urbanized area in Hangzhou City, China, as a case study, and collected stream water quality data and algae biomass in a field campaign. The results showed that built-up lands had negative effects on water quality and were the primary cause of stream water pollution. The concentration of total phosphorus significantly correlated with the areas of residential, industrial, road, and urban greenspace, and the concentration of chlorophyll a also significantly correlated with the areas of these land uses, except residential land. At a landscape level, the correlation analysis showed that the landscape indices, e.g., dominance, shape complexity, fragmentation, aggregation, and diversity, all had significant correlations with water quality parameters. From the perspective of land use, the redundancy analysis results showed that the percentages of variation in water quality explained by the built-up, forest and wetland, cropland, and bareland decreased in turn. The spatial composition of the built-up lands was the main factor causing stream water pollution, while the shape complexities of the forest and wetland patches were negatively correlated with stream water pollution.

Evaluation of geostatistical techniques and their hybrid in modelling of groundwater quality index in the Marand Plain in Iran

In many parts of the world, groundwater is considered as one of the main sources of urban and rural drinking water. Over the past three decades, the qualitative and quantitative characteristics of aquifers have been negatively affected by different factors such as excessive use of chemical fertilizers in agriculture, indiscreet, and over-exploitation use of groundwater. Therefore, finding the effective method for mapping the water quality index (WQI) is important for locating suitable and non-suitable areas for urban and rural drinking waters. In the present paper, the best method to estimate the spatial distribution of WQI was assessed using the inverse distance weighted, kriging, cokriging, geographically weighted regression (GWR), and hybrid models. Creating hybrid models can increase modeling capabilities. Hybrid methods make use of a combination of estimated model capabilities. In addition, to improve the results of cokriging, GWR, and hybrid methods, the auxiliary parameters of land slope, groundwater table, and groundwater transmissibility were used. In order to assess the proposed methodology, 11 qualitative parameters obtained from 63 observation wells in Marand Plain (Iran) were utilized. Four statistical measures, namely the root mean square error (RMSE), the mean absolute error (MAE), the Akaike coefficient (AIC), and the correlation coefficient (R²) along with the Taylor diagram, have been done. Classification of the WQI index showed that the quality of a number of 1, 27, 18, and 17 wells was, respectively, in excellent, good, moderate, and poor grades. The results of modeling the WQI index based on IDW, kriging, cokriging, GWR, and hybrid methods showed that the best estimate of WQI was obtained by using hybrid GWR-kriging method with three input parameters of land slope, groundwater table, and groundwater transmissibility. Therefore, hybrid kriging and GWR methods have been fairly well able to simulate the WQI index.

Hydrogen and Oxygen Isotope Composition and Water Quality Evaluation for Different Water Bodies in the Ebinur Lake Watershed, Northwestern China

Wetlands are sensitive indicators of climate change and have a profound impact on the supply of water resources in surrounding areas. In this study, the hydrochemical, isotopic characteristics (δ18O and δ2H) of groundwater and surface water (lake, reservoir, and river) in the Ebinur Lake Watershed, northwestern China, were investigated to reveal the relationships between various water bodies. The results suggest that the groundwater is alkaline and has pH and total dissolved solids (TDS) values less than those of surface water. Ca2+ and SO42− are the major ions in the groundwater and river water, whereas lake water and reservoir water are enriched in Na+ and SO42−. With the decrease in elevation, both groundwater and river water are affected by carbonate dissolution at high elevation and by evaporitic rock dissolution at low elevation; thus, the water surrounding Ebinur Lake is subjected to runoff affected by intense evaporation–dissolution of evaporitic rocks. The stable isotope compositions suggested that the upstream part of the river is recharged by glacial meltwater from high mountains, whereas the middle–downstream parts of the river are recharged by low-elevation precipitation. Shallow groundwater and reservoir water are mainly recharged by river water and are more enriched in the downstream part of river. Water samples were also classified according to different indices, such as chemical oxygen demand (COD), NH3-N, volatile phenol, sulfate, Zn, Co, Cu, total hardness, and Cr6+, and results showed that most groundwater is suitable for drinking and irrigation purposes. Except for Cr6+, the metal concentrations are within permissible limits. However, both groundwater and reservoir water are affected to some extent by nearby rivers from anthropogenic activity.