Spatial variations in the relationships between land use and water quality across an urbanization gradient in the watersheds of Northern Georgia, USA

Effect of urbanization and urban forests on water quality improvement in the Yangtze River Delta: A case study in Hangzhou, China

In the context of rapid global urbanization, the sustainable development of ecosystems should be considered. Accordingly, the Planetary Boundaries theory posits that reducing the amount of nitrogen and phosphorus pollutants entering bodies of water is necessary as excess levels may harm the aquatic environment and reduce in water quality. Thus, based on the long-term monitoring data of representative urban rivers in the Yangtze River Delta region, we evaluated the nitrogen and phosphorus pollution of water bodies in different urbanization stages and further quantified the effect of urban forests on water quality improvement. The results showed that, with the continuous progression of urbanization, the proportion of impervious surface area increased, along with the levels of nitrogen and phosphorus pollution in water bodies. The critical period of water quality deterioration in urban rivers occurred during the medium urbanization level when the proportion of impervious surface area reached 55-65 %, and the probability of an abrupt increase in total nitrogen (TN) and total phosphorus (TP) concentration exceeded 95 %. However, increasing the area of urban forests during this period reduced TN pollution by 36.64 % and TP pollution by 49.03 %. The results of this study support the expansion of urban forests during the medium urbanization stage to improve water quality. Furthermore, our results provide a reference and theoretical basis for urban forest construction as a key aspect of the sustainable development of the urban ecosystem in the Yangtze River Delta and similar regions around world.

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.

Driving Force Analysis of Natural Wetland in Northeast Plain Based on SSA-XGBoost Model

Globally, natural wetlands have suffered severe ecological degradation (vegetation, soil, and biotic community) due to multiple factors. Understanding the spatiotemporal dynamics and driving forces of natural wetlands is the key to natural wetlands' protection and regional restoration. In this study, we first investigated the spatiotemporal evolutionary trends and shifting characteristics of natural wetlands in the Northeast Plain of China from 1990 to 2020. A dataset of driving-force evaluation indicators was constructed with nine indirect (elevation, temperature, road network, etc.) and four direct influencing factors (dryland, paddy field, woodland, grassland). Finally, we built the driving force analysis model of natural wetlands changes to quantitatively refine the contribution of different driving factors for natural wetlands' dynamic change by introducing the sparrow search algorithm (SSA) and extreme gradient boosting algorithm (XGBoost). The results showed that the total area of natural wetlands in the Northeast Plain of China increased by 32% from 1990 to 2020, mainly showing a first decline and then an increasing trend. Combined with the results of transfer intensity, we found that the substantial turn-out phenomenon of natural wetlands occurred in 2000-2005 and was mainly concentrated in the central and eastern parts of the Northeast Plain, while the substantial turn-in phenomenon of 2005-2010 was mainly located in the northeast of the study area. Compared with a traditional regression model, the SSA-XGBoost model not only weakened the multicollinearity of each driver but also significantly improved the generalization ability and interpretability of the model. The coefficient of determination (R2) of the SSA-XGBoost model exceeded 0.6 in both the natural wetland decline and rise cycles, which could effectively quantify the contribution of each driving factor. From the results of the model calculations, agricultural activities consisting of dryland and paddy fields during the entire cycle of natural wetland change were the main driving factors, with relative contributions of 18.59% and 15.40%, respectively. Both meteorological (temperature, precipitation) and topographic factors (elevation, slope) had a driving role in the spatiotemporal variation of natural wetlands. The gross domestic product (GDP) had the lowest contribution to natural wetlands' variation. This study provides a new method of quantitative analysis based on machine learning theory for determining the causes of natural wetland changes; it can be applied to large spatial scale areas, which is essential for a rapid monitoring of natural wetlands' resources and an accurate decision-making on the ecological environment's security.

Distribution characteristics and potential release risk of nitrogen in sediments in Lake Daihai, China

Nitrogen (N) in sediments was a key element of lake eutrophication. The spatial distribution characteristics of four parts N in surface sediments were investigated by sequential extraction method, including free nitrogen (FN), exchangeable nitrogen (EN), hydrolyzable nitrogen (HN), and residual nitrogen (RN). Modified models were utilized to describe the adsorption isotherms of ammonia nitrogen (NH₄⁺-N) in sediments and thus predict the risk of N release. The correlation between environmental factors and N concentration was discussed, as well as the migration or transformation and release risk of N between different mediums. The results showed that spatial variations characteristics of N fractions were influenced by the lake topography and surrounding human activities. The content of total nitrogen (TN) in the sediments was 933.4 ~ 3006.8 mg/kg, with an average of 1835 mg/kg. The HN, RN, FN, and EN in sediments accounted for 66.85%, 21.35%, 6.82%, and 4.92% of TN, respectively. There was a significant correlation between each fraction of N and TN and also between different fractions of N (p < 0.01). Fitting by modified Langmuir model indicated that the adsorbed amounts of N in the sediments at maximum (Q_max) was, from greatest to least, southeast lake (2905.3 mg/kg) > southwest lake (1415.4 mg/kg) ≈ north lake (1424.6 mg/kg). Environmental parameters (pH, DO, C/N, etc.) affected the occurrence fraction of nitrogen, which could cause the persistent and increased risk of sustained release of high concentrations of endogenous N. N pollution in sediment and interstitial water is severe, and the risk of endogenous N release will gradually increase in the future.

Forested watersheds provide the highest water quality among all land cover types, but the benefit of this ecosystem service depends on landscape context

Conversion of natural land cover can degrade water quality in water supply watersheds and increase treatment costs for Public Water Systems (PWSs), but there are few studies that have fully evaluated land cover and water quality relationships in mixed use watersheds across broad hydroclimatic settings. We related upstream land cover (forest, other natural land covers, development, and agriculture) to observed and modeled water quality across the southeastern US and specifically at 1746 PWS drinking water intake facilities. While there was considerable complexity and variability in the relationship between land cover and water quality, results suggest that Total Nitrogen (TN), Total Phosphorus (TP) and Suspended Sediment (SS) concentrations decrease significantly with increasing forest cover, and increase with increasing developed or agricultural cover. Catchments with dominant (>90 %) agricultural land cover had the greatest export rates for TN, TP, and SS based on SPARROW model estimates, followed by developed-dominant, then forest- and other-natural-dominant catchments. Variability in modeled TN, TP, and SS export rates by land cover type was driven by variability in natural background sources and catchment characteristics that affected water quality even in forest-dominated catchments. Both intake setting (i.e., run-of-river or reservoir) and upstream land cover were important determinants of water quality at PWS intakes. Of all PWS intakes, 15 % had high raw water quality, and 85 % of those were on reservoirs. Of the run-of-river intakes with high raw water quality, 75 % had at least 50 % forest land cover upstream. In addition, PWS intakes obtaining surface water supply from smaller upstream catchments may experience the largest losses of natural land cover based on projections of land cover in 2070. These results illustrate the complexity and variability in the relationship between land cover and water quality at broad scales, but also suggest that forest conservation can enhance the resilience of drinking water supplies.

The spatial stress of urban land expansion on the water environment of the Yangtze River Delta in China

In highly urbanized and industrialized areas, the demand for construction land is expanding, which should have an impact on the water environment. Taking the Yangtze River Delta (YRD) and considering chemical oxygen demand (COD) and ammonia nitrogen (NH₃-N) as characteristic pollutants, this study investigated the spatial-temporal characteristics of water pollutant emissions at the county level, optimized the spatial lag model (SLM) to estimate the spatial interaction of urban expansion and water pollutant emissions through direct and indirect effects. The results show that from 2011 to 2015, water pollutant emissions in the YRD decreased significantly and that the high-emissions pattern changed from a contiguous to a scattered distribution. The emissions of COD and NH₃-N in counties at various distances from the Yangtze River and coastline show a logarithmic curve relationship. The association between urban expansion and water pollutant emissions was significant and stable. In 2015, every 1% increase in the scale of urban expansion resulted in 0.299% and 0.340% increases in local COD and NH₃-N emissions, respectively, and emissions in the adjacent counties synchronously increased by 0.068% and 0.084%, respectively. The results show that to break the association and spatial interaction between urban expansion and water pollutant emissions and alleviate the environmental stress on the YRD, in addition to delimiting an urban expansion boundary and strictly restraining the scale of expansion, improvement in the regional environmental carrying capacity through urban water pollutant treatment facilities and pipe network construction is urgently needed.

Land use, hydrology, and climate influence water quality of China's largest river

Influences of multiple environmental factors on water quality patterns is less studied in large rivers. Landscape analysis, multiple statistical methods, and the water quality index (WQI) were used to detect water quality patterns and influencing factors in China's largest river, the Yangtze River. Compared with the dry season, the wet season had significantly higher total phosphorus (TP), chemical oxygen demand (COD), total suspended solids (TSS), and turbidity (TUR). The WQI indicated "Moderate" and "Good" water quality in the wet and dry seasons, respectively. Compared with other sites, the upper reach sites that immediately downstream of the Three Gorges Dam had lower TP, TN, TSS and TUR in both seasons, and had lower and higher water temperature in the wet and dry seasons, respectively. Water quality patterns were mainly driven by heterogeneity in land use (i.e., wetland, cropland, and urban land), hydrology (i.e., water flow, water level), and climate (i.e., rainfall, air temperature). Water quality in the wet season was primarily driven by land use while the joint effect of land use and hydrology primarily drove in the dry season. Decision-makers and regulators of large river basin management may need to develop programs that consider influences from both human and natural drivers for water quality conservation.

Evaluation of Pollution Status and Detection of the Reason for the Death of Fish in Chamo Lake, Ethiopia

Chamo Lake is the third largest rift valley lake and one of the major economic sources for the communities in the Southern region, Ethiopia. The lake's quality is deteriorating due to the untreated wastewater, and sediment inflow resulting in the death of fish was observed during the dry season. The research aims to determine the water quality status using water quality indices, in addition to identifying the reason for the death of fish in the dry season in Chamo Lake. The water samples were drawn from 5 sampling points by composite sampling method during the dry and wet seasons of the year, and we analyzed 22 water quality parameters. Ammonia (14–23.6 mg/l), phosphates (0.30–1.10 mg/l), BOD (25.32–60 mg/l), COD (40–160 mg/l), and chlorophyll (19.64–31.87 μg/L) concentrations were above the permissible limits, and DO (5.20–6.70 mg/l) was below the acceptable limit in the lake as per EPA standards concerning temperature. The values of both the water quality indices of CCMEWQI (13.90–18.40) and NSFWQI (38.59–49.63) indicated that the water quality was “poor” and “bad,” respectively. The death of fish might be due to high concentrations of ammonia and nutrients in the dry season.

Long-term seasonal and temporal changes of hydrogen peroxide from cyanobacterial blooms in fresh waters

In water, hydrogen peroxide (H₂O₂) is produced through abiotic and biotic reactions with organic matter, including algal cells. The production of H₂O₂ is influenced by harmful algal cell communities and toxicity. However, only a few studies have been conducted on H₂O₂ concentrations in natural water. Particularly, the seasonal and temporal patterns of H₂O₂ concentration suggest that H₂O₂ generation from aquatic microorganisms could be identified to compare of photochemical production from dissolved organic matter. Study area is a source of raw water and is a large artificial lake located near a metropolitan city. Due to various environmental conditions, harmful algal blooms frequently occur in summer. The purpose of this study was to trace the H₂O₂ concentration and water quality parameters of study area where algal bloom occurs and what factors directly affect the H₂O₂ concentration. Experiments were performed on the influencing factors via water samples from study area and lab-scale culture tank. The lake produces an average of 553 nM H₂O₂, which increases by more than three times (1460 nM) in summer compared the winter. The lake (18.6-23.8 nMh^-1) produced more H₂O₂ than streams (7.4-9.0 nMh^-1) during daylight hours. All water sites presented the lowest production rates in dark conditions (1.1-1.5 nMh^-1). Daytime environment increased the generation rate more than the nighttime. The trend of H₂O₂ produced by algal cells was similar to that of the growth of algal cells. The exposure to external substances (heavy metals and antibiotics) increased the incidence by approximately five times; antibiotics were more influential than heavy metals.

Investigation of trace metals in riverine waterways of Bangladesh using multivariate analyses: spatial toxicity variation and potential health risk assessment

Minute quantities of trace metals have delirious effects in the human body causing acute and chronic toxicities. These trace metals have the ability to bind with enzymes and proteins causing an alteration in their activity, and, consequently, their damage. In this study, water samples were collected from five sites in Rupsa River (Bangladesh) during dry and wet seasons aiming to assess the trace metal concentration and the correlated health risk for people living in the area. Six trace metals, namely arsenic (Ar), cadmium (Cd), chromium (Cr), copper (Cu), lead (Pb), and nickel (Ni), were measured for further analyzing their spatial and seasonal variations. The measured trace metal concentrations followed this decreasing order: Cr > Pb > As > Cu > Ni > Cd for the dry season, and Cr > Pb > As > Cu > Ni > Cd for the wet season. Among the trace metals, As, Ni, Cr, and Pb exhibited a statistically significative variation throughout the study period. The PCA analysis accounted for 64.5% and 64.4% total variations of the trace metals in dry and wet seasons, respectively. The Euclidean distance of trace metals in water samples across five sites showed significantly different distribution patterns, which were further confirmed by PERMANOVA. Furthermore, CAP model disclosed that trace metals are source-specific: brickfields and sewage effluents were potential sources for Cd, whereas different industries were potential sources for As, Cr, Cu, Ni, and Pb. Correlation analysis showed that Ni and Cr significantly correlated with pH and electrical conductivity. Correlation among the trace metals unveiled that they depended on each other as for their origin, magnitude, and existence in the riverine waterways. As for the health risk assessment, a non-carcinogenic health hazard due to ingestion during regular activities and dermal contact during fishing activity to all kind of people (adult males, adult females, and children) in the studied area was retrieved based on the hazard index (HI) of trace metals, which was higher than the recommended value (HI > 1). Moreover, also the carcinogenic risks of Ni and As due to regular activities via ingestion and dermal contact pathways were higher than the standard value (CR > 1.0E-04), suggesting the occurrence of cancer risk to humans in the study area.

Stream sediment bacterial communities exhibit temporally-consistent and distinct thresholds to land use change in a mixed-use watershed

Freshwater ecosystems are susceptible to biodiversity losses due to land conversion. This is particularly true for the conversion of land from forests for agriculture and urban development. Freshwater sediments harbor microorganisms that provide vital ecosystem services. In dynamic habitats like freshwater sediments, microbial communities can be shaped by many processes, although the relative contributions of environmental factors to microbial community dynamics remain unclear. Given the future projected increase in land use change, it is important to ascertain how associated changes in stream physico-chemistry will influence sediment microbiomes. Here, we characterized stream chemistry and sediment bacterial community composition along a mixed land-use gradient in West Virginia, USA across one growing season. Sediment bacterial community richness was unaffected by increasing anthropogenic land use, though microbial communities were compositionally distinct across sites. Community threshold analysis revealed greater community resilience to agricultural land use than urban land use. Further, predicted metagenomes suggest differences in potential microbial function across changes in land use. The results of this study suggest that low levels of urban land use change can alter sediment bacterial community composition and predicted functional capacity in a mixed-use watershed, which could impact stream ecosystem services in the face of global land use change.

Using Remote Sensing and Multivariate Statistics in Analyzing the Relationship between Land Use Pattern and Water Quality in Tien Giang Province, Vietnam

This study was carried out to understand how land use patterns influence surface water quality in Tien Giang Province using remote sensing and statistical approaches. Surface water quality data were collected at 34 locations with the frequency of four times (March, June, September, and November) in 2019. Water quality parameters were used in the analysis, including pH, temperature, electrical conductivity (EC), total suspended solids (TSS), dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), ammonium (N-NH4+), nitrite (N-NO2−), nitrate (N-NO3−), sulfate (SO42−), orthophosphate (P-PO43−), chloride (Cl−), total nitrogen (TN), total phosphorus (TP), and coliform. The relationship between land use patterns and water quality was analyzed using geographic information techniques (GIS), remote sensing (RS), statistical approaches (cluster analysis (CA), principal component analysis (PCA), and Krustal–Wallis), and weighted entropy. The results showed water quality was impaired by total suspended solids, nutrients (N-NH4+, N-NO2−, P-PO43−), organic matters (BOD, COD), and ions (Cl− and SO42−). Kruskal–Wallis analysis results showed that all water quality parameters in the water bodies in Tien Giang Province were seasonally fluctuated, except for BOD and TN. The highest levels of water pollutants were found mostly in the dry season (March and June). The majority of the land in the study area was used for rice cultivation (40.64%) and residential (27.51%). Water quality in the study area was classified into nine groups corresponding to five combined land use patterns comprising residential–aquaculture, residential–rice cultivation, residential–perennials, residential–rice–perennial, and residential–rice–perennial crops–aquacultural. The concentrations of the water pollutants (TSS, DO, BOD, COD, N-NH4+, N-NO2−, Cl−, and coliform) in the locations with aquaculture land use patterns (Clusters 1 and 2) were significantly larger than those of the remaining land use patterns. PCA analysis presented that most of the current water quality monitoring parameters had a great impact on water quality in the water bodies. The entropy weight showed that TSS, N-NO2−, and coliform are the most important water quality parameters due to residential–aquaculture and residential–rice cultivation; EC, DO, N-NH4+, N-NO2−, Cl−, and coliform were the significant variables for the land use type of residential–perennial crops; N-NO2−, P-PO43−, and coliform for the land use pattern of residential–rice cultivation–perennial crops) and N-NH4+, N-NO2−, Cl−, and coliform for the land use pattern of residential–rice cultivation–perennial crops–aquaculture. The current findings showed that that surface water quality has been influenced by the complex land use patterns in which residential and rice cultivation may have major roles in causing water impairment. The results of the water quality assessment and the variation in water properties of the land use patterns found in this study provide scientific evidence for future water quality management.

Comparison between water quality indices in watersheds of the Southern Bahia (Brazil) with different land use

The present study evaluated the influence of land use and occupation on water quality indices (WQI); the WQI developed by the National Sanitation Foundation (NSF), the WQI adapted by the Environmental Company of the São Paulo State (CETESB), WQI proposed by Bascarón and the Canadian Council of Ministers of the Environment (CCME) WQI, obtained for watersheds located in the Eastern Water Planning and Management Region (BA). The study also analyzed the divergences and similarities of these WQI methods. Water quality data were obtained from the Monitoring Program (Monitora) of Environment and Water Resources Institute of Bahia (INEMA), covering the period from 2008 to 2015, at thirteen (13) sampling sites, with quarterly collections, as well as land use and occupation data. The influence of land use and occupation on water quality indices was assessed by principal component analysis (PCA). The PCA showed that urban and agricultural/pasture areas were influencing factors on water quality variables, such as total phosphorus, biochemical oxygen demand, total nitrogen, turbidity total residues and consequently lower WQI values in the Cachoeira watershed. Among the tested methods to evaluate the water quality of watersheds in the study area, the most similar were the NSF WQI, CETESB WQI, and Objective Bascarón WQI.

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.

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.

Small Reservoirs, Landscape Changes and Water Quality in Sub-Saharan West Africa

Small reservoirs (SRs) are essential water storage infrastructures for rural populations of Sub-Saharan West Africa. In recent years, rapid population increase has resulted in unprecedented land use and land cover (LULC) changes. Our study documents the impacts of such changes on the water quality of SRs in Burkina Faso. Multi-temporal Landsat images were analyzed to determine LULC evolutions at various scales between 2002 and 2014. Population densities were calculated from downloaded 2014 population data. In situ water samples collected in 2004/5 and 2014 from selected SRs were analyzed for Suspended Particulate Matter (SPM) loads, an integrative proxy for water quality. The expansion of crop and artificial areas at the expense of natural covers controlled LULC changes over the period. We found a very significant correlation between SPM loads and population densities calculated at a watershed scale. A general increase between the two sampling dates in the inorganic component of SPM loads, concomitant with a clear expansion of cropland areas at a local scale, was evidenced. Results of the study suggest that two complementary but independent indicators (i.e., LULC changes within 5-km buffer areas around SRs and demographic changes at watershed scale), relevantly reflected the nature and intensity of overall pressures exerted by humans on their environment, and locally on aquatic ecosystems. Recommendations related to the re-greening of peripheral areas around SRs in order to protect water bodies are suggested.

Spatial-temporal characteristics of nitrogen degradation in typical Rivers of Taihu Lake Basin, China

In this study, we focus on the measurement of different nitrogen (N) forms and investigate the spatial-temporal variability of degradation coefficient in river channels. We aim to provide a new approach of deriving in-situ degradation coefficients of different N forms, and highlight factors that determine the spatial-temporal variability of degradation coefficients. Our results are based on a two-year field survey in 34 channels around the Taihu Lake Basin, eastern China. The derived degradation coefficients of different N forms based our newly-developed experimental device are: degradation coefficients of TN, NH₄⁺-N and NO₃-N range from 0.006-0.449 d^-1, 0.022-1.175 d^-1 and -0.096-2.402 d^-1, respectively. The degradation coefficients of N show strong dependence on N concentration and water temperature. The seasonal difference of water temperature and N concentration leads to spatial-temporal variability of degradation coefficients. The derived degradation coefficients of N are further verified through one-dimensional water quality model simulations. The degradation coefficient obtained in this study and the influencing factors of its spatial-temporal variability provide invaluable reference for studies in aquatic environment.

Linking Changes in Land Cover and Land Use of the Lower Mekong Basin to Instream Nitrate and Total Suspended Solids Variations

Population growth and economic development are driving changes in land use/land cover (LULC) of the transboundary Lower Mekong River Basin (LMB), posing a serious threat to the integrity of the river system. Using data collected on a monthly basis over 30 years (1985–2015) at 14 stations located along the Lower Mekong river, this study explores whether spatiotemporal relationships exist between LULC changes and instream concentrations of total suspended solids (TSS) and nitrate—as proxies of water quality. The results show seasonal influences where temporal patterns of instream TSS and nitrate concentrations mirror patterns detected for discharge. Changes in LULC influenced instream TSS and nitrate levels differently over time and space. The seasonal Mann–Kendall (SMK) confirmed significant reduction of instream TSS concentrations at six stations (p < 0.05), while nitrate levels increased at five stations (p < 0.05), predominantly in stations located in the upper section of the basin where forest areas and mountainous topography dominate the landscape. Temporal correlation analyses point to the conversion of grassland (r = −0.61, p < 0.01) to paddy fields (r = 0.63, p < 0.01) and urban areas (r = 0.44, p < 0.05) as the changes in LULC that mostly impact instream nitrate contents. The reduction of TSS appears influenced by increased forest land cover (r = −0.72, p < 0.01) and by the development and operation of hydropower projects in the upper Mekong River. Spatial correlation analyses showed positive associations between forest land cover and instream concentrations of TSS (r = 0.64, p = 0.01) and nitrate (r = 0.54, p < 0.05), indicating that this type of LULC was heavily disturbed and harvested, resulting in soil erosion and runoff of nitrate rich sediment during the Wet season. Our results show that enhanced understanding of how LULC changes influence instream water quality at spatial and temporal scales is vital for assessing potential impacts of future land and water resource development on freshwater resources of the LMB.

Mapping Urbanization and Evaluating Its Possible Impacts on Stream Water Quality in Chattanooga, Tennessee, Using GIS and Remote Sensing

Impervious surfaces (IS) produced by urbanization can facilitate pollutants’ movement to nearby water bodies through stormwater. This study mapped and estimated the IS changes in Chattanooga, Tennessee, using satellite imagery acquired in 1986 and 2016. A model was developed utilizing the Normalized Difference Vegetation Index coupled with density slicing to detect and map urbanization through IS growth. Urban growth was quantified at USGS HUC12 watershed level including stream riparian areas. The obtained results show a net growth of 45.12 km2 of IS with a heterogeneous distribution. About 9.96 km2 of this growth is within 90 m of streams, about 6% of the study site’s land cover. The Lower South Chickamauga Creek watershed experienced the largest urban growth with a change from 24.2 to 48.5 km2. Using the riparian zone percent imperviousness, a stream risk assessment model was developed to evaluate potential stream impairment due to this growth. Approximately 87, 131, and 203 km lengths of streams identified as potentially at high, very high, and extreme risks, respectively, to be impaired due to urban growth from the last 30 years. These findings would benefit to proactively implement sustainable management plans for the streams near rapidly urbanizing areas in the study site.

1H-NMR Determination of Organic Compounds in Municipal Wastewaters and the Receiving Surface Waters in Eastern Cape Province of South Africa

Surface water is the recipient of pollutants from various sources, including improperly treated wastewater. Comprehensive knowledge of the composition of water is necessary to make it reusable in water-scarce environments. In this work, proton nuclear magnetic resonance (¹H-NMR) was combined with multivariate analysis to study the metabolites in four rivers and four wastewater treatment plants releasing treated effluents into the rivers. ¹H-NMR chemical shifts of the extracts in CDCl were acquired with Bruker 400. Chemical shifts of ¹H-NMR in chlorinated alkanes, amino compounds and fluorinated hydrocarbons were common to samples of wastewater and lower reaches or the rivers. ¹H-NMR chemical shifts of carbonyl compounds and alkyl phosphates were restricted to wastewater samples. Chemical shifts of phenolic compounds were associated with treated effluent samples. This study showed that the sources of these metabolites in the rivers were not only from improperly treated effluents but also from runoffs. Multivariate analyses showed that some of the freshwater samples were not of better quality than wastewater and treated effluents. Observations show the need for constant monitoring of rivers and effluent for the safety of the aquatic environment.

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.

Assessing long-term urban surface water changes using multi-year satellite images: A tale of two cities, Dhaka and Hong Kong

Water is a fundamental component of an urban environment. Management of water resources is important to facilitate a liveable environment and urban sustainability. Several factors affect water resources, including urbanization, climate change and seasons. Moreover, the nature of urban expansion and unsustainable water management practices have been associated with water scarcity, loss of biodiversity and increase of flood risk. Knowledge of the changes in urban surface water in relation to changes in seasons, land covers, anthropogenic activities, and topographical characteristics are important for managing watersheds and urban planning, and developing adaptation strategies to address environmental challenges posed by urbanization. However, existing studies rarely consider all the above factors when monitoring surface water changes in the urban environment. To address this problem, this study uses satellite images from multiple seasons and years, and assess the changes in surface water in relation to changes of several important factors, e.g., seasons, urbanization, land cover and topography. Moreover, this study applies advanced geostatistical tools to assess the local relationship between changes in surface water and the driving factors, and compares the findings in two cities Dhaka (Bangladesh) and Hong Kong with a large contrast in many aspects. First, seasonal influence in the distribution of water area is evaluated. Second, land cover classifications are assigned, and then the contingency matrix and decadal maps are formulated to investigate and evaluate the influence of urbanization on the occurrence and transitions in surface water. Third, an advanced geostatistical regression model is used to investigate the spatially varying relationships for change in surface water in relation to change in other land covers and topography. The investigations confirm that the temporal and seasonal variation, and urbanization induced land cover changes largely affect surface water. In addition, topography influences the nature of the city's expansion, which in turn entails the changes in surface water. The flat terrain of Dhaka facilitates the easy invasion of water bodies and horizontal expansion, in contrast, the steep terrain of Hong Kong prohibits horizontal expansion thus it's surface water is more stable than Dhaka.

Integration Multi-Model to Evaluate the Impact of Surface Water Quality on City Sustainability: A Case from Maanshan City in China

Water pollution is a worldwide problem that needs to be solved urgently and has a significant impact on the efficiency of sustainable cities. The evaluation of water pollution is a Multiple Criteria Decision-Making (MCDM) problem and using a MCDM model can help control water pollution and protect human health. However, different evaluation methods may obtain different results. How to effectively coordinate them to obtain a consensus result is the main aim of this work. The purpose of this article is to develop an ensemble learning evaluation method based on the concept of water quality to help policy-makers better evaluate surface water quality. A valid application is conducted to illustrate the use of the model for the surface water quality evaluation problem, thus demonstrating the effectiveness and feasibility of the proposed model.

Distribution and source analysis of heavy metal pollutants in sediments of a rapid developing urban river system

Heavy metal pollution of aquatic environments in rapidly developing industrial regions is of considerable global concern due to its potential to cause serious harm to aquatic ecosystems and human health. This study assessed heavy metal contamination of sediments in a highly industrialized urban watershed of eastern China containing several historically unregulated manufacturing enterprises. Total concentrations and solid-phase fractionation of Cu, Zn, Pb, Cr and Cd were investigated for 39 river sediments using multivariate statistical analysis and geographically weighted regression (GWR) methods to quantitatively examine the relationship between land use and heavy metal pollution at the watershed scale. Results showed distinct spatial patterns of heavy metal contamination within the watershed, such as higher concentrations of Zn, Pb and Cd in the southwest and higher Cu concentration in the east, indicating links to specific pollution sources within the watershed. Correlation and PCA analyses revealed that Zn, Pb and Cd were dominantly contributed by anthropogenic activities; Cu originated from both industrial and agricultural sources; and Cr has been altered by recent pollution control strategies. The GWR model indicated that several heavy metal fractions were strongly correlated with industrial land proportion and this correlation varied with the level of industrialization as demonstrated by variations in local GWR R² values. This study provides important information for assessing heavy metal contaminated areas, identifying heavy metal pollutant sources, and developing regional-scale remediation strategies.

Increasing chloride in rivers of the conterminous U.S. and linkages to potential corrosivity and lead action level exceedances in drinking water

Corrosion in water-distribution systems is a costly problem and controlling corrosion is a primary focus of efforts to reduce lead (Pb) and copper (Cu) in tap water. High chloride concentrations can increase the tendency of water to cause corrosion in distribution systems. The effects of chloride are also expressed in several indices commonly used to describe the potential corrosivity of water, the chloride-sulfate mass ratio (CSMR) and the Larson Ratio (LR). Elevated CSMR has been linked to the galvanic corrosion of Pb whereas LR is indicative of the corrosivity of water to iron and steel. Despite the known importance of chloride, CSMR, and LR to the potential corrosivity of water, monitoring of seasonal and interannual changes in these parameters is not common among water purveyors. We analyzed long-term trends (1992-2012) and the current status (2010-2015) of chloride, CSMR, and LR in order to investigate the short and long-term temporal variability in potential corrosivity of US streams and rivers. Among all sites in the trend analyses, chloride, CSMR, and LR increased slightly, with median changes of 0.9mgL^-1, 0.08, and 0.01, respectively. However, urban-dominated sites had much larger increases, 46.9mgL^-1, 2.50, and 0.53, respectively. Median CSMR and LR in urban streams (4.01 and 1.34, respectively) greatly exceeded thresholds found to cause corrosion in water distribution systems (0.5 and 0.3, respectively). Urbanization was strongly correlated with elevated chloride, CSMR, and LR, especially in the most snow-affected areas in the study, which are most likely to use road salt. The probability of Pb action-level exceedances (ALEs) in drinking water facilities increased along with raw surface water CSMR, indicating a statistical connection between surface water chemistry and corrosion in drinking water facilities. Optimal corrosion control will require monitoring of critical constituents reflecting the potential corrosivity in surface waters.

Combining citizen science and land use data to identify drivers of eutrophication in the Huangpu River system

In recent years, the massive land use changes and urbanization of Shanghai City have coincided with a growing eutrophication and an overall degradation of Huangpu River, with related risks to the city's drinking water supply and economic development. However, there is only limited information to evaluate the spatial and temporal changes to the Huangpu River and its many tributaries. In the present study, 400 citizen scientists were trained to monitor water quality and environmental conditions on a monthly basis over three years in the lower (high urbanized) Huangpu River catchment. Their data were integrated with high resolution land cover data using GIS techniques to characterize water quality dynamics of the Huangpu River system with respect to main environmental drivers. Environmental driver analysis indicated that up-catchment conditions dominate river dynamics while typical urban impacts (first flush, impermeable land cover…) have only limited influence. According to these results, the city's investments to improve wastewater treatment and mitigate lower river impacts need to be extended throughout the catchment to reduce nutrient concentrations that are near or above thresholds for rivers and streams. The positive impact of in-stream vegetation pointed to the possibilities that local scale ecological remediation activities to reduce runoff could be viable approaches to improve river conditions throughout the catchment.

Spatial patterns and origins of heavy metals in Sheyang River catchment in Jiangsu, China based on geographically weighted regression

Multivariate statistical analyses combined with geographically weighted regression (GWR) were used to identify spatial variations of heavy metals in sediments and to examine relationships between metal pollution and land use practices in watersheds, including urban land, agriculture land, forest and water bodies. Seven metals (Cu, Zn, Pb, Cr, Ni, Mn and Fe) of sediments were measured at 31 sampling sites in Sheyang River. Most metals were under a certain degree enrichment based on the enrichment factors. Cluster analysis grouped all sites into four statistically significant cluster, severely contaminated areas were concentrated in areas with intensive human activities. Correlation analysis and PCA indicated Cu, Zn and Pb were derived from anthropogenic activities, while the sources of Cr and Ni were complicated. However, Fe and Mn originated from natural sources. According to results of GWR, there are stronger association between metal pollution with urban land than agricultural land and forest. Moreover, the relationships between land use and metal concentration were affected by the urbanization level of watersheds. Agricultural land had a weak associated with heavy metal pollution and the relationships might be stronger in less-urbanized. This study provided useful information for the assessment and management of heavy metal hazards in studied area.

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

As an important regulator of pollutants in overland flow and interflow, land use has become an essential research component for determining the relationships between surface water quality and pollution sources. This study investigated the use of ordinary least squares (OLS) and geographically weighted regression (GWR) models to identify the impact of land use and population density on surface water quality in the Wen-Rui Tang River watershed of eastern China. A manual variable excluding-selecting method was explored to resolve multicollinearity issues. Standard regression coefficient analysis coupled with cluster analysis was introduced to determine which variable had the greatest influence on water quality. Results showed that: (1) Impact of land use on water quality varied with spatial and seasonal scales. Both positive and negative effects for certain land-use indicators were found in different subcatchments. (2) Urban land was the dominant factor influencing N, P and chemical oxygen demand (COD) in highly urbanized regions, but the relationship was weak as the pollutants were mainly from point sources. Agricultural land was the primary factor influencing N and P in suburban and rural areas; the relationship was strong as the pollutants were mainly from agricultural surface runoff. Subcatchments located in suburban areas were identified with urban land as the primary influencing factor during the wet season while agricultural land was identified as a more prevalent influencing factor during the dry season. (3) Adjusted R² values in OLS models using the manual variable excluding-selecting method averaged 14.3% higher than using stepwise multiple linear regressions. However, the corresponding GWR models had adjusted R² ~59.2% higher than the optimal OLS models, confirming that GWR models demonstrated better prediction accuracy. Based on our findings, water resource protection policies should consider site-specific land-use conditions within each watershed to optimize mitigation strategies for contrasting land-use characteristics and seasonal variations.

Micro and Macroscale Drivers of Nutrient Concentrations in Urban Streams in South, Central and North America

Global metrics of land cover and land use provide a fundamental basis to examine the spatial variability of human-induced impacts on freshwater ecosystems. However, microscale processes and site specific conditions related to bank vegetation, pollution sources, adjacent land use and water uses can have important influences on ecosystem conditions, in particular in smaller tributary rivers. Compared to larger order rivers, these low-order streams and rivers are more numerous, yet often under-monitored. The present study explored the relationship of nutrient concentrations in 150 streams in 57 hydrological basins in South, Central and North America (Buenos Aires, Curitiba, São Paulo, Rio de Janeiro, Mexico City and Vancouver) with macroscale information available from global datasets and microscale data acquired by trained citizen scientists. Average sub-basin phosphate (P-PO₄) concentrations were found to be well correlated with sub-basin attributes on both macro and microscales, while the relationships between sub-basin attributes and nitrate (N-NO₃) concentrations were limited. A phosphate threshold for eutrophic conditions (>0.1 mg L^-1 P-PO₄) was exceeded in basins where microscale point source discharge points (eg. residential, industrial, urban/road) were identified in more than 86% of stream reaches monitored by citizen scientists. The presence of bankside vegetation covaried (rho = –0.53) with lower phosphate concentrations in the ecosystems studied. Macroscale information on nutrient loading allowed for a strong separation between basins with and without eutrophic conditions. Most importantly, the combination of macroscale and microscale information acquired increased our ability to explain sub-basin variability of P-PO₄ concentrations. The identification of microscale point sources and bank vegetation conditions by citizen scientists provided important information that local authorities could use to improve their management of lower order river ecosystems.

Land use/land cover water quality nexus: quantifying anthropogenic influences on surface water quality

Anthropogenic forces widely influence the composition, configuration, and trend of land use and land cover (LULC) changes with potential implications for surface water quality. These changes have the likelihood of generating non-point source pollution with additional environmental implications for terrestrial and aquatic ecosystems. Monitoring the scope and trajectory of LULC change is pivotal for region-wide planning, tracking the sustainability of natural resources, and meeting the information needs of policy makers. A good comprehension of the dynamics of anthropogenic drivers (proximate and underlying) that influence such changes in LULC is important because any potential adverse change in LULC that may be inimical to sustainable water quality might be addressed at the anthropogenic driver level rather than the LULC change stage. Using a dense time stack of Landsat-5 Thematic Mapper images, a hydrologic water quality and socio-geospatial modeling framework, this study quantifies the role of anthropogenic drivers of LULC change on total suspended solids and total phosphorus concentrations in the Lower Chippewa River Watershed, Wisconsin, at three time steps-1990, 2000, and 2010. Results of the study demonstrated that proximate drivers of LULC change accounted for between 32 and 59% of the concentration and spatial distribution of total suspended solids, while the extent of phosphorus impairment attributed to the proximate drivers ranged between 31 and 42%.

Modeling climate change, urbanization, and fire effects on Pinus palustris ecosystems of the southeastern U.S

Managing ecosystems for resilience and sustainability requires understanding how they will respond to future anthropogenic drivers such as climate change and urbanization. In fire-dependent ecosystems, predicting this response requires a focus on how these drivers will impact fire regimes. Here, we use scenarios of climate change, urbanization and management to simulate the future dynamics of the critically endangered and fire-dependent longleaf pine (Pinus palustris) ecosystem. We investigated how climate change and urbanization will affect the ecosystem, and whether the two conservation goals of a 135% increase in total longleaf area and a doubling of fire-maintained open-canopy habitat can be achieved in the face of these drivers. Our results show that while climatic warming had little effect on the wildfire regime, and thus on longleaf pine dynamics, urban growth led to an 8% reduction in annual wildfire area. The management scenarios we tested increase the ecosystem's total extent by up to 62% and result in expansion of open-canopy longleaf by as much as 216%, meeting one of the two conservation goals for the ecosystem. We find that both conservation goals for this ecosystem, which is climate-resilient but vulnerable to urbanization, are only attainable if a greater focus is placed on restoration of non-longleaf areas as opposed to maintaining existing longleaf stands. Our approach demonstrates the importance of accounting for multiple relevant anthropogenic threats in an ecosystem-specific context in order to facilitate more effective management actions.

Temporal and spatial variations in the relationship between urbanization and water quality

With the development of economy, most of Chinese cities are at the stage of rapid urbanization in recent years, which has caused many environmental problems, especially the serious deterioration of water quality. Therefore, the research of the relationship between urbanization and water quality has important theoretical and practical significance, and it is also the main restriction factor in the urbanization advancement. In this work, we investigated the impact of urbanization on the water quality of the nearby river. We established a comprehensive environmental assessment framework by combining urbanization and water quality, and one model was designed to examine the impact of urbanization on the water quality in Jinan from 2001 to 2010 with factor component analysis. The assessment of urbanization level was accomplished using a comprehensive index system, which was based on four aspects: demographic urbanization, economic urbanization, land urbanization, and social urbanization. In addition, synthetic pollution index method was utilized to assess the water pollution of Xiaoqing River in the study area. Through the analysis of regression curves, we conclude that (1) when the urbanization level is below 25 %, the relationship is low and irregular; (2) if the urbanization level varies between 25 and 40 %, there will be an irreversible degradation of stream water quality; (3) there is a positive correlation between urbanization and pollution levels of urban river after the adjustment period; and (4) land and demographic aspects have the highest independent contribution. This study is a useful reference for policymakers in terms of economic and environmental management.

Urban stream syndrome in a small, lightly developed watershed: a statistical analysis of water chemistry parameters, land use patterns, and natural sources

The relationships among land use patterns, geology, soil, and major solute concentrations in stream water for eight tributaries of the Kayaderosseras Creek watershed in Saratoga County, NY, were investigated using Pearson correlation coefficients and multivariate regression analysis. Sub-watersheds corresponding to each sampling site were delineated, and land use patterns were determined for each of the eight sub-watersheds using GIS. Four land use categories (urban development, agriculture, forests, and wetlands) constituted more than 99 % of the land in the sub-watersheds. Eleven water chemistry parameters were highly and positively correlated with each other and urban development. Multivariate regression models indicated urban development was the most powerful predictor for the same eleven parameters (conductivity, TN, TP, NO[Formula: see text], Cl(-), HCO(-)3, SO9(2-)4, Na(+), K(+), Ca(2+), and Mg(2+)). Adjusted R(2) values, ranging from 19 to 91 %, indicated that these models explained an average of 64 % of the variance in these 11 parameters across the samples and 70 % when Mg(2+) was omitted. The more common R (2), ranging from 29 to 92 %, averaged 68 % for these 11 parameters and 72 % when Mg(2+) was omitted. Water quality improved most with forest coverage in stream watersheds. The strong associations between water quality variables and urban development indicated an urban source for these 11 water quality parameters at all eight sampling sites was likely, suggesting that urban stream syndrome can be detected even on a relatively small scale in a lightly developed area. Possible urban sources of Ca(2+) and HCO(-)3 are suggested.

Application of geographically weighted regression model to analysis of spatiotemporal varying relationships between groundwater quantity and land use changes (case study: Khanmirza Plain, Iran)

Understanding the spatiotemporal relationships between land use/cover changes (LUCC) and groundwater resources is necessary for effective and efficient land use management. In this paper, geographically weighted regression (GWR) and ordinary least squares (OLS) models have been expanded to analyze varying spatial relationships between groundwater quantity changes and LUCC for three periods: 1987-2000, 2000-2010, and 1987-2010 in the Khanmirza Plain of southwestern Iran. For this purpose, TM images were used to generate LUCC (rainfed, irrigated, meadow, and bare lands). Groundwater quantity variables, including groundwater level changes (GLC) and groundwater withdrawal differences (GWD), were gathered from piezometric and agricultural wells data. The analysis of spatial autocorrelation (Moran's I and local indicators of spatial association ) demonstrated that GWR has a better ability to model spatially varying data with very minimal clustering of residuals. The results R (2) and corrected Akaike's Information Criterion parameters revealed that the GWR has the lowest similarity in space and time in neighboring situations and it has the high ability to explain more variance in the LUCC as a function of the groundwater quantity changes. All results of the distribution of local R (2) values from GWR confirm our assertion that there is a spatiotemporal relationship between types of land use and each of groundwater quantity variables within the region. According to the t test results from GWR, there are significant differences between the GLC and GWD and the land use types in different places of region in each of the three time series. The GWR results can help decision-makers to make appropriate decisions for future planning.

Coupled effects of natural and anthropogenic controls on seasonal and spatial variations of river water quality during baseflow in a coastal watershed of Southeast China

Surface water samples of baseflow were collected from 20 headwater sub-watersheds which were classified into three types of watersheds (natural, urban and agricultural) in the flood, dry and transition seasons during three consecutive years (2010–2012) within a coastal watershed of Southeast China. Integrating spatial statistics with multivariate statistical techniques, river water quality variations and their interactions with natural and anthropogenic controls were examined to identify the causal factors and underlying mechanisms governing spatiotemporal patterns of water quality. Anthropogenic input related to industrial effluents and domestic wastewater, agricultural activities associated with the precipitation-induced surface runoff, and natural weathering process were identified as the potential important factors to drive the seasonal variations in stream water quality for the transition, flood and dry seasons, respectively. All water quality indicators except SRP had the highest mean concentrations in the dry and transition seasons. Anthropogenic activities and watershed characteristics led to the spatial variations in stream water quality in three types of watersheds. Concentrations of NH₄⁺-N, SRP, K⁺, COD_Mn, and Cl⁻ were generally highest in urban watersheds. NO₃^–N Concentration was generally highest in agricultural watersheds. Mg²⁺ concentration in natural watersheds was significantly higher than that in agricultural watersheds. Spatial autocorrelations analysis showed similar levels of water pollution between the neighboring sub-watersheds exhibited in the dry and transition seasons while non-point source pollution contributed to the significant variations in water quality between neighboring sub-watersheds. Spatial regression analysis showed anthropogenic controls played critical roles in variations of water quality in the JRW. Management implications were further discussed for water resource management. This research demonstrates that the coupled effects of natural and anthropogenic controls involved in watershed processes, contribute to the seasonal and spatial variation of headwater stream water quality in a coastal watershed with high spatial variability and intensive anthropogenic activities.