Effective Water Quality Management Research Articles

Risk assessment via oral and dermal pathways from heavy metal polluted water of Kolleru lake - A Ramsar wetland in Andhra Pradesh, India.

The Kolleru Lake, India is a famous wetland of international significance. Analyses of certain potentially toxic heavy metal ions in water indicate that this freshwater lake is characterized by highly heterogeneous distribution of chromium (Cr; 4.5–80 μg/L), copper, iron (Cu, Fe; below detection limit), manganese (Mn; 1–313 μg/L) and zinc (Zn; below detection limit). Non-carcinogenic health risk assessment indices like hazard quotients (HQ) and hazard indices (HI) are estimated following the guidelines recommended by the US Environmental Protection Agency (USEPA). These indices are found to be within the acceptable limit (<1), indicating negligible potential health risk via ingestion and dermal routes. However, when the average values of these indices pertaining to the Kolleru lake are normalized with similar estimates from clean and uncontaminated global surface water, both high and low ratios are obtained. While Cr (12.5), Cu (2.3) and Mn (3.7) exhibit high ratios, those of Fe (0.09) and Zn (0.99) show respectively low and comparable values. The significance of such heterogeneous distribution of hazard indices and their ratios are discussed. Further, average carcinogenic risk levels of the adults and children due to Cr ingestion are estimated to be 0.00154 and 0.0022, respectively. Both values are higher than the permissible levels recommended by the USEPA. As a remediation measure, it is recommended that monitoring the levels of heavy metal in water and other items like fish in the lake or rice and vegetables grown in the area is needed to be carried out at regular intervals. This study therefore offers requisite perception to the local government and health officials to evolve their plan of action so that effective management and mitigation of water quality of the Kolleru lake can be administered.

Simulation-based interval chance-constrained quadratic programming model for water quality management: A case study of the central Grand River in Ontario, Canada

Effective river water quality management and planning is a complex issue challenged by various complexities and uncertainties. A simulation-based interval chance-constrained quadratic programming (ICCQP) model is developed for the seasonal planning of water quality management (WQM) under various uncertainties. The proposed model incorporates interval quadratic programming, chance-constrained programming, and a seasonal water quality simulation model within a general framework for WQM. Uncertainties associated with the objective and the coefficients in the left-hand sides of the constraints are tackled as intervals. Meanwhile, parameter uncertainties on the right-hand sides are characterized using probability distributions. Nonlinearities in the cost function are reflected by quadratic programming. A multi-segment water quality model is used to simulate the dynamic interactions between wastewater discharges and river water quality. The proposed ICCQP-WQM model is applied in a real case study for the control of total phosphorus (TP) in the central Grand River in Ontario, Canada. The results demonstrate that the proposed model is able to incorporate uncertainties expressed as intervals and probability information into an optimization framework and provide interval solutions. Thus, different cost-effective schemes for seasonal WQM could be generated. The results show the Kitchener wastewater treatment plant (WWTP) affects the value of the objective function more than the other WWTPs in the study area. It is also found that the Kitchener WWTP's cost accounts for the highest proportion (approximately 35.1–37.9%) of the total annual cost, which implies the control of TP at the Kitchener plant is the most important to the system. Moreover, river water TP standards in spring and autumn are usually difficult to meet, indicating different TP control strategies are needed in these two seasons. The generated results are valuable for local decision makers to generate TP control strategies, and also to identify optimized solutions under various uncertainties. The proposed ICCQP-WQM model can be extended to other watersheds to support effective water quality management and planning.

Assessing changes in nitrogen contamination in groundwater using water aging: Waikato River, New Zealand

Groundwater and surface water contamination by nitrogen can produce serious health issues particularly regarding the contraction of methemoglobinemia in infants; prevention of this disorder is a main reason for regulating drinking water quality in New Zealand. To assist the management of water quality in the Upper Waikato Catchment in the light of growing intensification of nitrogen producing land uses, a regional groundwater flow and contamination transport model was developed to assess probable future nitrogen concentrations. Calibration of the steady state model was achieved with a root mean square error of 4.7% for groundwater levels and 7.9% for gains and losses in river flow within the catchment. Model performance was assisted by relationships determined between SiO2 concentrations in groundwater and water age. Modelling coupled with water aging indicates that the mean residence time of contributing water in the Upper Waikato Catchment ranges from 5 to 101 years with an average of 50 years. Some 77% of surface water is sourced from groundwater which is likely to convey the predominant contribution to the total nitrogen load. Nitrogen concentrations are increasing but the long-term total load is difficult to estimate owing largely to lack of information about historical land use and the degree of attenuation of nitrogen. The model and results obtained may be employed to provide information on water travel time distributions, appropriate source location and surface water receiving reaches in rivers and streams all of which is important and necessary for effective water quality management in the Catchment.

Genetic-Algorithm-Optimized Sequential Model for Water Temperature Prediction

Advances in establishing real-time river water quality monitoring networks combined with novel artificial intelligence techniques for more accurate forecasting is at the forefront of urban water management. The preservation and improvement of the quality of our impaired urban streams are at the core of the global challenge of ensuring water sustainability. This work adopted a genetic-algorithm (GA)-optimized long short-term memory (LSTM) technique to predict river water temperature (WT) as a key indicator of the health state of the aquatic habitat, where its modeling is crucial for effective urban water quality management. To our knowledge, this is the first attempt to adopt a GA-LSTM to predict the WT in urban rivers. In recent research trends, large volumes of real-time water quality data, including water temperature, conductivity, pH, and turbidity, are constantly being collected. Specifically, in the field of water quality management, this provides countless opportunities for understanding water quality impairment and forecasting, and to develop models for aquatic habitat assessment purposes. The main objective of this research was to develop a reliable and simple urban river water temperature forecasting tool using advanced machine learning methods that can be used in conjunction with a real-time network of water quality monitoring stations for proactive water quality management. We proposed a hybrid time series regression model for WT forecasting. This hybrid approach was applied to solve problems regarding the time window size and architectural factors (number of units) of the LSTM network. We have chosen an hourly water temperature record collected over 5 years as the input. Furthermore, to check its robustness, a recurrent neural network (RNN) was also tested as a benchmark model and the performances were compared. The experimental results revealed that the hybrid model of the GA-LSTM network outperformed the RNN and the basic problem of determining the optimal time window and number of units of the memory cell was solved. This research concluded that the GA-LSTM can be used as an advanced deep learning technique for time series analysis.

Assessing seasonal nitrogen export to large tropical lakes

Rivers are exporting increasing amounts of nitrogen (N) to lakes, which is leading to eutrophication. However, the seasonality apparent in nutrient loading, especially in tropical areas, is thus far only partially understood. This study aims to better understand the seasonality and the sources of dissolved inorganic nitrogen (DIN) inputs from sub-basins to tropical lakes. We integrated existing approaches into a seasonal model that accounts for seasonality in human activities, meteorology and hydrology, and we applied the model to the sub-basins of a representative tropical lake: Lake Tana, Ethiopia. The model quantifies the river export of DIN by season, source and sub-basin and also accounts for open defecation to land as a diffuse source of N in rivers. Seasonality parameters were calibrated, and model outputs were validated against measured nitrogen loads in the main river outlets. The calibrated model showed good agreement with the measured nitrogen loads at the outflow of the main rivers. The model distinguishes four seasons: rainy (July–September), post-rainy (October–December), dry (January–March) and pre-rainy (April–June). The river export of DIN to Lake Tana was about 9 kton in 2017 and showed spatial and temporal variability: It was highest in the rainy and lowest in the dry seasons. Diffuse sources from agriculture were important contributors of DIN to rivers in 2017, and animal manure was the dominant source in all seasons. Our seasonal sub-basins and rivers model provides opportunities to identify the main nutrient sources to the lake and to formulate effective water quality management options. An example is nutrient application level that correspond to the crop needs in the sub-basins. Furthermore, our model can be used to analyse future trends and serves as an example for other large tropical lakes experiencing eutrophication.

Advancing Understanding of Land Use and Physicochemical Impacts on Fecal Contamination in Mixed-Land-Use Watersheds

Understanding mixed-land-use practices and physicochemical influences on Escherichia (E.) coli concentrations is necessary to improve water quality management and human health. Weekly stream water samples and physicochemical data were collected from 22 stream gauging sites representing varying land use practices in a contemporary Appalachian watershed of the eastern USA. Over the period of one annual year, Escherichia (E.) coli colony forming units (CFU) per 100 mL were compared to physicochemical parameters and land use practices. Annual average E. coli concentration increased by approximately 112% from acid mine drainage (AMD) impacted headwaters to the lower reaches of the watershed (approximate averages of 177 CFU per 100 mL vs. 376 CFU per 100 mL, respectively). Significant Spearman’s correlations (p &lt; 0.05) were identified from analyses of pH and E. coli concentration data representing 77% of sample sites; thus highlighting legacy effects of historic mining (AMD) on microbial water quality. A tipping point of 25–30% mixed development was identified as leading to significant (p &lt; 0.05) negative correlations between chloride and E. coli concentrations. Study results advance understanding of land use and physicochemical impacts on fecal contamination in mixed-land-use watersheds, aiding in the implementation of effective water quality management practices and policies.

IDENTIFICATION OF INFLUENCING PARAMETERS OF WATER QUALITY AND ITS ANALYSIS BY USING SPATIAL GRAPHS AND AUTOCORRELATION FUNCTION

Spatial analysis or spatial statistics includes many techniques and study the entities like geographic properties. Spatial analysis has variety of techniques using different approaches applied to various fields most notably in the analysis of geographic data. Defining the spatial location of the entities being studied is the fundamental problem in the spatial analysis. Spatial graphs and mathematical tools play a vital role in the analysis of spatial data. The integration of Graph theory, statistical tools and remote sensing with GIS provides a scientific platform for developing an integrated database among all the different entities involved in environmental planning and management activities. In this connection, the advanced technology of remote sensing and GIS software linking with the water quality data to create the spatial distribution and spatial graphs for identification of water quality stretch zones and its impact is discussed. Specifically, considered spatial statistics, spatial autocorrelation, and spatial graph theory are considered to identify the water quality index. For effective management and sustainable development of water quality the influence of various parameters are studied.

Distributions of enterococci and human-specific bacteriophages of enterococci in a tropical watershed.

The bacteriophages of E. faecalis strains AIM06 (DSM100702) and SR14 (DSM100701) have previously been validated as human-specific microbial source tracking (MST) markers in Thailand. In this study, their spatial and temporal distribution in a freshwater river was investigated for the first time (n=48). The abundance of enterococci as a standard microbial water quality parameter was evaluated by both the qPCR detection assay with primers and a hydrolysis probe according to the US EPA Method 1611 and the US EPA Method 1600 membrane filtration culture method. AIM06 and SR14 phages were detected by a double layer agar assay and were present in 87.5% and 81.3% of all samples with a co-presence of 92.9% of phage-positive samples. After spiking the representative phages, the ranges of recovery efficiencies were 57.9-99.6% and 49.6-99.9% (n=48) for AIM06 and SR14 phages, respectively. The absolute abundance of AIM06 and SR14 phages ranged from 0.25 to 221.94 and from 0.25 to 76.66PFU/100mL, respectively. Enterococci DNA copies and CFU were detected in all samples ranging from 3.24 to 6.32 log10 copies/100mL and 100.00 to 1593CFU/100mL, respectively. Enterococci in the qPCR assay also showed a moderate correlation with the culture method. The AIM06 and SR14 phage results indicated continuing human faecal pollution along the river with no significant different levels among stations. Interestingly, the higher levels of enterococci in downstream stations for both the qPCR and culture methods along with the significant correlation with other faecal indicator organisms and non-human MST markers implied non-human faecal pollution. In conclusion, this study provides insightful information that could lead to effective water quality management and public health risk reduction from exposure to faecally-contaminated water.

Recent Advances in Information and Communications Technology (ICT) and Sensor Technology for Monitoring Water Quality

Water quality control and management in water resources are important for providing clean and safe water to the public. Due to their large area, collection, analysis, and management of a large amount of water quality data are essential. Water quality data are collected mainly by manual field sampling, and recently real-time sensor monitoring has been increasingly applied for efficient data collection. However, real-time sensor monitoring still relies on only a few parameters, such as water level, velocity, temperature, conductivity, dissolved oxygen (DO), and pH. Although advanced sensing technologies, such as hyperspectral images (HSI), have been used for the areal monitoring of algal bloom, other water quality sensors for organic compounds, phosphorus (P), and nitrogen (N) still need to be further developed and improved for field applications. The utilization of information and communications technology (ICT) with sensor technology shows great potential for the monitoring, transmission, and management of field water-quality data and thus for developing effective water quality management. This paper presents a review of the recent advances in ICT and field applicable sensor technology for monitoring water quality, mainly focusing on water resources, such as rivers and lakes, and discusses the challenges and future directions.

Shifts in coralline algae, macroalgae, and coral juveniles in the Great Barrier Reef associated with present-day ocean acidification.

Seawater acidification from increasing CO2 is often enhanced in coastal waters due to elevated nutrients and sedimentation. Our understanding of the effects of ocean and coastal acidification on present-day ecosystems is limited. Here we use data from three independent large-scale reef monitoring programs to assess coral reef responses associated with changes in mean aragonite saturation state (Ωar ) in the Great Barrier Reef World Heritage Area (GBR). Spatial declines in mean Ωar are associated with monotonic declines in crustose coralline algae (up to 3.1-fold) and coral juvenile densities (1.3-fold), while non-calcifying macroalgae greatly increase (up to 3.2-fold), additionally to their natural changes across and along the GBR. These three key groups of organisms are important proxies for coral reef health. Our data suggest a tipping point at Ωar 3.5-3.6 for these coral reef health indicators. Suspended sediments acted as an additive stressor. The latter suggests that effective water quality management to reduce suspended sediments might locally and temporarily reduce the pressure from ocean acidification on these organisms.

Effect of local watershed landscapes on the nitrogen and phosphorus concentrations in the waterbodies of reservoir bays.

Reservoir bays, which are affected by the reservoir and watershed characteristics, are the initial and most sensitive areas in the evolution process of reservoir water quality. However, the relationship between the watershed characteristics and nitrogen and phosphorus concentrations in reservoir bays is poorly understood. We selected 66 bays from the Danjiangkou Reservoir and sampled twice per year (storage and discharge periods) from 2015 to 2018 to monitor the total nitrogen (TN) and total phosphorus (TP) concentration in the waterbodies of the reservoir bays. Four types of watershed characteristic indices (topographic variables, soil variables, land-use composition, and landscape patterns) around these bays were obtained. We quantified the relationship between the TN and TP concentrations and watershed characteristics in the waterbodies of the reservoir bays using partial least squares regression (PLSR). The results showed that the mean concentrations of TN and TP in the storage period (TN:1.69mg·L-1, TP:0.088mg·L-1) were higher than those in the discharge period (TN:1.22mg·L-1, TP:0.063mg·L-1). The optimal PLSR models explained 67.9% and 82.5% of the TN concentration variability, and 65.4% and 67.2% of the TP concentration variability during the storage and discharge period, respectively. Based on the variable importance in the projection (VIP) values, soil erodibility had significant effects on the TN and TP concentrations. The key factors affecting the TN concentration were the slope gradient, basin relief, topographic wetness index, forest and agricultural land use, whereas the factors controlling the TP concentration were the landscape shape index, edge density, Shannon's diversity index and grass land use, although the TP concentration was also controlled by the patch density and contagion during the storage period, and by mean patch size and largest patch index during the discharge period. This study provides critical insights into sustainable landscape planning and effective reservoir water quality management.

Automatic calibration and improvements on an instream chlorophyll a simulation in the HSPF model

Accurate prediction of chlorophyll a (Chl a) concentration in surface water bodies such as lakes or rivers is crucial for water quality management. This study improved the predictive simulation of instream Chl a with the Hydrological Simulation Program-FORTRAN (HSPF) by adding automatic calibration and modifying the growth-temperature formulation of phytoplankton in the original HSPF model. A total of 62 model parameters, selected from a series of sensitivity analyses, were automatically calibrated in a stepwise manner for different variables in the order of flow, sediment, water temperature, ammonia/nitrate couple, and phosphate/Chl a couple. With finer temporal resolution (5–8 days) data than those of majority of the existing HSPF studies, the automatic calibration procedure provided the model with performance ratings of ‘satisfactory’ or better for all the variables including nutrients and Chl a: The percent bias values ranged from -18% - 54% and -20% – 62% for nutrients and Chl a, respectively. The original linear equation on the growth-temperature relationship of phytoplankton in simulating instream Chl a was modified using a quadratic equation and an exponential equation. The exponential equation outperformed the original linear and quadratic equations, particularly in simulating the excess concentrations of Chl a observed during summer seasons. For the validation data set, the exponential equation predicted 78% of the eutrophic cases while the linear and quadratic equation only predicted 53% and 13% of the eutrophic cases, respectively. The modified HSPF model offers an improved prediction of instream Chl a. This approach will be useful for providing early warning of algal blooms, facilitating the implementation of effective management of stream water quality.

Rainfall-driven E. coli transfer to the stream-conduit network observed through increasing spatial scales in mixed land-use paddy farming karst terrain

Karst aquifers have distinctive hydrology and supply 25% of the world’s population with drinking water, making them a critical geological setting for understanding and managing microbial water pollution. Rainfall causes elevated concentrations and loading of faecal microorganisms, e.g. E. coli, in catchment surface and groundwater systems, increasing the risk of human exposure to faecally-contaminated water. However, effective management of microbial water quality in complex karst catchments is constrained by limited understanding of E. coli - discharge responses to rainfall. We analysed how rainfall events of varying magnitude (2.4–100 mm) control E. coli-discharge dynamics at increasing spatial scales in a mixed land-use karst catchment in southwest China. During the wet season, hourly water sampling was undertaken throughout five storm events to characterise in high detail E. coli emergence with resulting flow across multiple sites of varying catchment area, stream order, and land-use. E. coli concentration was found to increase by 1–3 orders of magnitude following rainfall events. Maximum E. coli concentration and speed of E. coli recession were influenced by rainfall (amount, intensity), timing of agricultural activities, and position in the hydrological system. For high intensity events ∼90% of the cumulative E. coli export occurred within 48 h. E. coli concentration increased with increasing discharge at all sites. E. coli concentration at low discharge was higher in the headwaters than at the catchment outlet, while the rate of increase in E. coli concentration with increasing discharge appears to follow the opposite trend, being higher at the catchment outlet than the headwaters. This was attributed to the decreasing flow path gradient and increasing degree of development of the fissure network, but further event monitoring at varying catchment scales is required to confirm this relationship. The results provide novel insight into how rainfall characteristics combine with land-use and catchment hydrology to control E. coli export in karst landscapes.

Evaluating the spatial scaling effect of baseflow and baseflow nonpoint source pollution in a nested watershed

Effective water-quality management requires full understanding of the nonpoint source (NPS) pollutant pathways. However, the spatial scaling effect has become one key barrier for the estimation of baseflow NPS pollutants. In this study, the scaling effect of baseflow NPS pollutants was investigated. The Soil and Water Assessment Tool was used for flow and NPS pollutants simulation at nested river sections, while the baseflow NPS pollutants was then estimated based on 17 levels of nested watersheds in a watershed of China. The results indicated that as the spatial scale increased, the baseflow became an important pathway of NPS pollutants, as its average contribution increased from 0.27 in the small headwater catchment to 0.45 in the nested large watershed. The scaling effects of the baseflow index (BFI) and the contribution of the baseflow NPS pollutants were closely related to the land use distribution among those upstream catchments, whereas for the mainstream, the tributary inflow would become another important reason for the scaling effect. Furthermore, the spatial scaling effect of the BFI was less obvious during the dry period, whereas the spatial scaling of the baseflow NPS pollutants did not show much difference among different hydrological periods.

Principal Component Analysis for Water Quality Assessment of the Ganga River in Uttar Pradesh, India

The present study is an attempt to apply principal component analysis (PCA) for spatial assessment of water quality parameters that are responsible for water quality deterioration in the Ganga river at four cities of Uttar Pradesh. 48 water samples of the Ganga river from Allahabad, Mirzapur, Shahzadpur and Varanasi in Uttar Pradesh were collected during January to December in 2013. Data were analyzed for assessment of 18 different physicochemical and biological water quality parameters. These variables were examined using PCA to define and standardize the parameters mainly responsible for water quality variance in Ganga river at four selected cities. PCA highlighted anthropogenic effect and industrial effect as two main significant components which explain more than 99.32% of the variance, accounting for 64.47 and 34.85% respectively of the total variance of Ganga water quality at the four cities. Results revealed that total dissolved solids, total alkalinity, total hardness were the important parameters in assessing variations in Ganga water quality during October to April (post-monsoon months) and turbidity, suspended solids are the important parameters in assessing variations in Ganga water quality during June to September (monsoon months). Ca2+, Cl–, $${\text{SO}}_{4}^{{2 - }}$$ , temperature, fluoride, pH, Fe, Cl–, were found to be non-principal water quality parameters. Principal component analysis produced three significant main components explaining more than 82.9% of the variance (anthropogenic and industrial effect) that present 57.1, 13.8 and 12% respectively of the total variance of water quality in Ganga river at the four selected cities. The result reveals that turbidity, dissolved oxygen and biochemical oxygen demand are the parameters that are most important in assessing variations of water quality. Water quality index based on eight parameters (turbidity, DO, BOD, COD, pH, TS, TSS and TDS) calculated for all four cities were found to range from medium to bad. Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis, interpretation of complex data sets and understanding spatial variations in water quality for effective river water quality management.

Climate change will pose challenges to water quality management in the st. Croix River basin

Responses of streamflow and nutrient export to changing climate conditions should be investigated for effective water quality management and pollution control. Using downscaled climate projections and the Soil and Water Assessment Tool (SWAT), we projected future streamflow, sediment export, and riverine nutrient export in the St. Croix River Basin (SCRB) during 2020–2099. Results show substantial increases in riverine water, sediment, and nutrient load under future climate conditions, particularly under the high greenhouse gas emission scenario. Intensified water cycling and enhanced nutrient export will pose challenges to water quality management and affect multiple Best Management Practices (BMPs) efforts, which are aimed at reducing nutrient loads in SCRB. In addition to the physical impacts of climate change on terrestrial hydrology, our analyses demonstrate significant reductions in ET under elevated atmospheric CO2 concentrations. Changes in plant physiology induced by climate change may markedly affect water cycling and associated sediment and nutrient export. Results of this study highlight the importance of examining climate change impacts on water and nutrient delivery for effective watershed management.

ASSESSMENT OF TRACE METALS USING CHEMOMETRIC ANALYSIS IN KUANTAN RIVER, EAST COAST MALAYSIA

Chemometric techniques were applied to identify and assess the trace metals of Kuantan River water. Water samples from nine sampling stations were taken from downstream of the estuary towards the upstream of Kuantan River. The following trace metals were selected for analysis i.e. Hg, Cd, Pb, As, Co and Cr. To evaluate chemical variation on the variables, multivariate statistical methods like principal components analysis (PCA), cluster analysis (CA) and discriminant analysis (DA) were performed. The homogeneity characteristics between the stations were integrated by cluster analysis, which were represented by dendrograms and were classified according to their surrounded activities. PCA was identified four factors namely Cr, Hg, Pb and Co explaining 77.8% of the total variance. PCA revealed that Kuantan River is strongly affected by point sources such as commercial wastewater, industries and non-point sources especially from agriculture activities. The CA classified three clusters as commercial centre (CC), residential areas (RA) and agricultural areas (AA). AA located in the upstream is less polluted than RA, while CC located in the downstream is more polluted than RA. These statistical approaches and results yielded useful information including identification of pollution sources/factors and understanding temporal variations in river water for effective river water quality management.

Managing nitrogen to restore water quality in China.

The nitrogen cycle has been radically changed by human activities1. China consumes nearly one third of the world's nitrogen fertilizers. The excessive application of fertilizers2,3 and increased nitrogen discharge from livestock, domestic and industrial sources have resulted in pervasive water pollution. Quantifying a nitrogen 'boundary'4 in heterogeneous environments is important for the effective management of local water quality. Here we use a combination of water-quality observations and simulated nitrogen discharge from agricultural and other sources to estimate spatial patterns of nitrogen discharge into water bodies across China from 1955 to 2014. We find that the critical surface-water quality standard (1.0 milligrams of nitrogen per litre) was being exceeded in most provinces by the mid-1980s, and that current rates of anthropogenic nitrogen discharge (14.5±3.1 megatonnes of nitrogen per year) to fresh water are about 2.7 times the estimated 'safe' nitrogen discharge threshold (5.2±0.7 megatonnes of nitrogen per year). Current efforts to reduce pollution through wastewater treatment and by improving cropland nitrogen management can partially remedy this situation. Domestic wastewater treatment has helped to reduce net discharge by 0.7±0.1 megatonnes in 2014, but at high monetary and energy costs. Improved cropland nitrogen management could remove another 2.3±0.3 megatonnes of nitrogen per year-about 25 per cent of the excess discharge to fresh water. Successfully restoring a clean water environment in China will further require transformational changes to boost the national nutrient recycling rate from its current average of 36 per cent to about 87 per cent, which is a level typical of traditional Chinese agriculture. Although ambitious, such a high level of nitrogen recycling is technologically achievable at an estimated capital cost of approximately 100 billion US dollars and operating costs of 18-29 billion US dollars per year, and could provide co-benefits such as recycled wastewater for crop irrigation and improved environmental quality and ecosystem services.

Assessment of water quality in and around Jia-Bharali river basin, North Brahmaputra Plain, India, using multivariate statistical technique

The present study envisages the application of multivariate analysis, water utility class and conventional graphical representation to reveal the hidden factor responsible for deterioration of water quality and determine the hydrochemical facies of water sources in Jia-Bharali river basin, North Brahmaputra Plain, India. Fifty groundwater and 35 surface water samples were collected and analyzed for 15 parameters viz pH, TDS, hardness, COD, Ca2+, Mg2+, Na+, K+, Fe, HCO3−, Cl−, SO42−, NO3−, PO43− and F− for a period of 3 hydrological years (2009–2011) in six different seasons (three wet and three dry). The results were evaluated and compared with WHO and BIS water quality standards. Except Fe (> 0.3 mg/L), all parameters were found well within the desirable limit of WHO and BIS for drinking water. Ca2+ and HCO3− were dominant ions among cations and anions. The piper trilinear diagram classified majority of water samples for both seasons fall in the fields of Ca2+–Mg2+–HCO3− water type indicating temporary hardness. Varimax factors extracted by principal component analysis indicates anthropogenic (domestic and agricultural runoff) and geogenic influences on the trace elements. Hierarchical cluster analysis grouped water sources into three statistically significant clusters based on the similarity of water quality characteristics. This study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex datasets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective water quality management.

Hysteresis analysis of nitrate dynamics in the Neuse River, NC

Anthropogenic activities have caused N saturation in many terrestrial ecosystems. The transfer of nutrients and sediments to freshwater environments has resulted in water quality impairments including eutrophication, increased turbidity, ecosystem acidification, and loss of biodiversity. Storm events account for the transport of a large proportion of nutrients and sediments found in watersheds on an annual basis. To implement effective water-quality management strategies, the importance of surface and subsurface flow paths during storm events and low flow conditions need to be quantified. The increased availability of optical in-situ sensors makes high-frequency monitoring of catchment fluxes practical. In this study, we present a high-resolution nitrate monitoring record over a 10-year period in the Neuse River Basin near Clayton, North Carolina. The relationship between discharge and nitrate concentration for 365 storm events are categorized into hysteresis classes that indicate different transport mechanisms into the river. Storm events over the entire period of this study are divided between clockwise, counter-clockwise, and complex hysteresis patterns, indicating multiple nitrate flow paths during different seasons and years. Logistic regression of a suite of environmental variables demonstrates that antecedent soil moisture is a significant factor in determining the storm hysteresis class, with the odds of counter-clockwise hysteresis increasing by 10.3% for every 1 percentage point increase in the soil moisture. There is also an overlying seasonal effect, which indicates that dry soil conditions and frequent small storms during summer leads to greater nitrate transport on the rising limb, in contrast to slower, groundwater-driven inputs during the rest of the year.