Development of the hybrid cells in series model to simulate ammonia nutrient pollutant transport along the Umgeni River

Discharge of organic waste results in high nutrient pollution of the water bodies which is a major menace to the environment. A high quantity of nutrients such as ammonia causes a reduction in the dissolved oxygen level and induces algal growth in the water bodies. Water quality models have been the tools to evaluate the rate at which streams can disperse the pollutants they receive. Many water quality models are flawed either because of their inadequacy to completely simulate the advection component of the pollutant transport, or because of the limited application of the models, due to inaccurate estimation of model parameters. The hybrid cell in series (HCIS) developed by Ghosh et al. (2004) has been able to overcome such difficulties associated with the mixing cell-based models. Thus, the current study focuses on developing an analytical solution for the pollutant transport of the ammonia concentration through the plug flow, the first and second well-mixed cells of the HCIS model. The HCIS model coupled with the first order kinetic equation for ammonia nutrient was developed to simulate the ammonia pollutant concentration in the water column. The ammonia concentration at various points along the river system was assessed by considering the effects of the transformation of ammonia to nitrite, the uptake of ammonia by the algae, the respiration rate of the algae and the input of benthic source to the ammonia concentration in the water column. The proposed model was tested using synthetic data, and the HCIS-NH₃ model simulations for spatial and temporal variation of ammonia pollutant transport were analysed. The simulated results of the HCIS-NH₃ model agreed with the Fickian-based advection-dispersion equation (ADE) for simulating ammonia concentration solved using an explicit finite difference scheme. The HCIS-NH₃ model also showed a good agreement with the observed data from the Umgeni River, except during rainy periods.     

Modelling the effects of water diversion and combined sewer overflow on urban inland river quality

In order to assist and optimize the operation of a clean water diversion project for the medium-sized inland rivers in Chaohu, China, an integrated hydrodynamic and water quality model was used in this study. Sixteen diversion scenarios and five sewage interception scenarios were defined to assess the improvement of water quality parameters including ammonia nitrogen (NH₃-N), total phosphorus (TP) and chemical oxygen demand (COD) under different diverted water flows, diverting times, diverting points, diverting routines and sewage interception proportions. An index of pollutant removal rate per unit diverted water flow (PRUWF) was proposed to evaluate the effect of the clean water diversion. Results show that operating conditions played important roles in water quality improvement of medium-sized inland rivers. The optimal clean water diversion was operated under the conditions of a flow rate of 5 m³/s for 48 h with an additional constructed bridge sluice. A global sensitivity analysis using the Latin Hypercube One-Factor-at-a-Time (LH-OAT) method was conducted to distinguish the contributions of various driving forces to inland river water restoration. Results show that sewage interception was more important than diverted water flow and diverting time with respect to water quality improvement, especially for COD.     

Application of non-linear automatic optimization techniques for calibration of HSPF.

Development of TMDLs (total maximum daily loads) is often facilitated by using the software system BASINS (Better Assessment Science Integrating point and Nonpoint Sources). One of the key elements of BASINS is the watershed model HSPF (Hydrological Simulation Program Fortran) developed by USEPA. Calibration of HSPF is a very tedious and time consuming task, more than 100 parameters are involved in the calibration process. In the current research, three non-linear automatic optimization techniques are applied and compared, as well an efficient way to calibrate HSPF is suggested. Parameter optimization using local and global optimization techniques for the watershed model is discussed. Approaches to automatic calibration of HSPF using the nonlinear parameter estimator PEST (Parameter Estimation Tool) with its Gauss-Marquardt-Levenberg (GML) method, Random multiple Search Method (RSM), and Shuffled Complex Evolution method developed at the University of Arizona (SCE-UA) are presented. Sensitivity analysis was conducted and the most and the least sensitive parameters were identified. It was noted that sensitivity depends on number of adjustable parameters. As more parameters were optimized simultaneously--a wider range of parameter values can maintain the model in the calibrated state. Impact of GML, RSM, and SCE-UA variables on ability to find the global minimum of the objective function (OF) was studied and the best variables are suggested. All three methods proved to be more efficient than manual HSPF calibration. Optimization results obtained by these methods are very similar, although in most cases RSM outperforms GML and SCE-UA outperforms RSM. GML is a very fast method, it can perform as well as SCE-UA when the variables are properly adjusted, initial guess is good and insensitive parameters are eliminated from the optimization process. SCE-UA is very robust and convenient to use. Logical definition of key variables in most cases leads to the global minimum.     

Spatiotemporal distributions of nutrients in the downstream from Gezhouba Dam in Yangtze River, China

Introduction

From December 2008 to November 2009, an investigation of water quality was performed in the 70-km long downstream from Gezhouba Dam in Yangtze River.

Methods

Twelve sites in all were chosen. Nine parameters of water quality including the total phosphorus, the total nitrogen, chlorophyll a (Chl.a), nitrite, nitrate, ammonia, water temperature, DO, and pH were monitored almost monthly. The multivariate statistical technique (cluster analysis) and the nonparametric method (Kruskal?CWallis Test and Spearman??s rank correlation) were applied to evaluate the spatiotemporal variations of water quality data sets.

Results and discussion

According to the Chinese environmental quality standards for surface water (GB3838-2002), the water quality in the river section investigated can attain to the III water standards except total nitrogen. Further analysis indicated that there were no significant spatial differences in these parameters of water quality, but the sampling date had a significant effect. The temporal variation of water quality can be related to the discharge of Gezhouba Dam and moreover be affected by the reservoir regulation. During the discharge, the discharge increased the concentration of total phosphorus and then decreased the N:P ratio, which helps to the occurrence of algae blooms. The high consternation of phosphorus and the low N:P ratio show that the water body can be in the process of eutrophication during the discharge of Gezhouba Dam. In fact, Chl.a had begun to rise in the same period.     

Optimization of a wet scrubber with electrolyzed water spray—Part I: Ammonia removal

Electrolyzed water (EW) is an effective disinfectant with a wide range of pH. EW in acid range was proved to be an ammonia absorbent which make it valuable for wet scrubbers used in animal feeding operations (AFOs). This study aimed to optimize the design and operating parameters of a wet scrubber with EW spray for ammonia removal, based on the size distribution of droplets, the property of EW and the reduction efficiency of ammonia. The optimized parameters included droplet size, nozzle flow rates, pH and available chlorine concentration (ACC) of EW, nozzle number at single stage, stage number, initial ammonia concentration and air speed in the duct. The ammonia removal efficiency increased with the decrease of droplet size and the increase of flow rate. The pH values of EW showed significant influence on ammonia removal efficiency (P ? 0.05), while ACC of the EW showed no significant influence (P > 0.05). For inlet ammonia concentration of 70 ppm with one and three spray stages, the wet scrubber with EW (pH = 1.35) spray was able to reduce 55.8 ± 4.3 % and 97.2 ± 3.0 % of ammonia, respectively, when the nozzles with 0.9 mm orifice diameter operated at a flow rate of 1.20 L min^-1. Response surface analysis showed that orifice diameter, nozzle flow rate, and their combination were all significant factors impacting ammonia removal efficiency for both pH =1.35 and 5.50 at a 95% confidence level. Optimal ammonia removal efficiency was obtained at orifice diameter 0.9 mm and flow rate 1.20 L min^-1 within the selected range. The results of this study demonstrated that wet scrubber with EW spray could be a very effective and feasible ammonia mitigation technology for animal feeding operation.

Implications: It is difficult to effectively reduce ammonia emitted from the animal feeding operations (AFOs). Both the acidity and disinfection effects of electrolyzed water (EW) make it a potential absorbent used for spray in wet scrubber to reduce the ammonia and microorganisms. Based on some preliminary field test results, lab tests were conducted to optimize the design and operation parameters of a wet scrubber with EW spray to improve the ammonia removal efficiency. A better understanding of the application and influence factors of the wet scrubber with EW spray can contribute to effective mitigation of ammonia emission from animal houses and improve the atmosphere air quality.     

Evaluation of spatial-temporal variations and trends in surface water quality across a rural-suburban-urban interface

Water quality degradation is often a severe consequence of rapid economic expansion in developing countries. Methods to assess spatial-temporal patterns and trends in water quality are essential for guiding adaptive management efforts aimed at water quality remediation. Temporal and spatial patterns of surface water quality were investigated for 54 monitoring sites in the Wen-Rui Tang River watershed of eastern China to identify such patterns in water quality occurring across a rural-suburban-urban interface. Twenty physical and chemical water quality parameters were analyzed in surface waters collected once every 4–8 weeks from 2000 to 2010. Temporal and spatial variations among water quality parameters were assessed between seasons (wet/dry) and among major land use zones (urban/suburban/rural). Factor analysis was used to identify parameters that were important in assessing seasonal and spatial variations in water quality. Results revealed that parameters related to organic pollutants (dissolved oxygen (DO), chemical oxygen demand (manganese) (COD_Mn), and 5-day biochemical oxygen demand (BOD₅)), nutrients (ammonia nitrogen (NH₄ ⁺-N), total nitrogen (TN), total phosphorus (TP)), and salt concentration (electrical conductivity (EC)) were the most important parameters contributing to water quality variation. Collectively, they explained 70.9 % of the total variance. A trend study using the seasonal Kendall test revealed reductions in COD_Mn, BOD₅, NH₄ ⁺-N, petrol, V-phen, and EC concentrations over the 11-year study period. Cluster analysis was employed to evaluate variation among 14 sampling sites representative of dominant land use categories and indicated three, three, and four clusters based on organic, nutrient, and salt water quality characteristics, respectively. Factors that are typically responsible for water quality degradation (including population, topography, and land use) showed no strong correlation with water quality trends implying considerable point source inputs in the watershed. The results of this study help inform ongoing water quality remediation efforts by documenting trends in water quality across various land use zones.     

Uncertainty Analysis of Hydrologic and Water Quality Predictions for a Small Watershed Using SWAT2000

Hydrologic and water quality (H/WQ) models are being used with increasing frequency to devise alternative pollution control strategies. It has been recognized that such models may have a large degree of uncertainty associated with their predictions, and that this uncertainty can significantly impact the utility of the model. In this study, ARRAMIS (Advanced Risk & Reliability Assessment Model) software package was used to analyze the uncertainty of the SWAT2000 (Soil and Water Assessment Tool) outputs concerning nutrients and sediment losses from agricultural lands. ARRAMIS applies Monte Carlo simulation technique connected with Latin hypercube sampling (LHS) scheme. This technique is applied to the Warner Creek watershed located in the Piedmont physiographic region of Maryland, and it provides an interval estimate of a range of values with an associated probability instead of a point estimate of a particular pollutant constituent. Uncertainty of model outputs was investigated using LHS scheme with restricted pairing for the model input sampling. Probability distribution functions (pdfs) for each of the 50 model simulations were constructed from these results. Model output distributions of interest in this analysis were stream flow, sediment, organic nitrogen (organic-N), organic phosphorus (organic-P), nitrate, ammonium, and mineral phosphorus (mineral-P) transported with water. Developed probability distribution functions for the model provided information with desirable probability. Results indicate that consideration of input parameter uncertainty produces 64% less mean stream flow along with approximately 8.2% larger sediment loading than obtained using mean input parameters. On the contrary, mean of outputs regarding nutrients such as nitrate, ammonia, organic-N, and organic-P (but not mineral-P) were almost the same as the one using mean input parameters. The uncertainty in predicted stream flow and sediment loading is large, but that for nutrient loadings is the same as that of the corresponding input parameters. This study concluded that using a best possible distribution for the input parameters to reflect the impact of soils and land use diversity in a small watershed on SWAT2000 model outputs may be more accurate than using average values for each input parameter.     

Assessment of Aquifer Vulnerability in an Agricultural Area in Spain Using the DRASTIC Model

The assessment of aquifer vulnerability is a very important task, especially in agricultural areas because the quality and availability of groundwater affects both the sustainability of agriculture and the quality of life. In this study, an integrated approach is considered, with the use of the generic and agricultural DRASTIC models as well as a geographic information system (GIS), to assess groundwater vulnerability in the agricultural area of Barrax, in the province of Albacete, in Spain. Seven parameters—depth to water, net recharge, aquifer media, soil media, topography, impact of vadose zone media, and hydraulic conductivity of the aquifer (DRASTIC)—have been considered as weighted layers to enable an accurate groundwater risk mapping. The results of the generic DRASTIC model indicated very low vulnerability to contamination for Barrax groundwater due to limited urban and industrial development in the wider area. However, agricultural activities impose pressure to groundwater resources and the results of the agricultural DRASTIC model show that 6.86% of the study area is characterized by very high, 2.29% by high, 47.28% by medium, 38.28% by low, and the remaining 5.29% by no vulnerability to groundwater contamination. The distribution of nitrate concentration in groundwater in the area under study is quite well correlated with the agricultural DRASTIC vulnerability index. Sensitivity analysis was also performed to acknowledge statistical uncertainty in the estimation of each parameter used, to assess its impact, and thus to identify the most critical parameters that require further investigation. Depth to water and impact of vadose zone are the parameters that had the most noticeable impact on the generic DRASTIC vulnerability index followed by the soil media and topography. In contrast, the agricultural DRASTIC method is more sensitive to the removal of the depth to water parameter followed by the topography and the soil media parameters.     

Uncertainty in Nonpoint Source Pollution Models and Associated Risks

The usefulness of water quality simulation models for environmental management is explored with a focus on prediction uncertainty. The specific objective is to demonstrate how the usability of a flow and transport model (here: MACRO) can be enhanced by developing and analyzing its output probability distributions based on input variability. This infiltration-based model was designed to investigate preferential flow effects on pollutant transport. A statistical sensitivity analysis is used to identify the most uncertain input parameters based on model outputs. Probability distribution functions of input variables were determined based on field-measured data obtained under alternative tillage treatments. Uncertainty of model outputs is investigated using a Latin hypercube sampling scheme (LHS) with restricted pairing for model input sampling. Probability density functions (pdfs) are constructed for water flow rate, atrazine leaching rate, total accumulated leaching, and atrazine concentration in percolation water. Results indicate that consideration of input parameter uncertainty produces a 20% higher mean flow rate along with two to three times larger atrazine leaching rate, accumulated leachate, and concentration than that obtained using mean input parameters. Uncertainty in predicted flow rate is small but that in solute transport is an order of magnitude larger than that of corresponding input parameters. Macropore flow is observed to contribute to the variability of atrazine transport results. Overall, the analysis provides a quantification of prediction uncertainty that is found to enhance a user's ability to assess risk levels associated with model predictions.     

Uncertainty in Nonpoint Source Pollution Models and Associated Risks

The usefulness of water quality simulation models for environmental management is explored with a focus on prediction uncertainty. The specific objective is to demonstrate how the usability of a flow and transport model (here: MACRO) can be enhanced by developing and analyzing its output probability distributions based on input variability. This infiltration-based model was designed to investigate preferential flow effects on pollutant transport. A statistical sensitivity analysis is used to identify the most uncertain input parameters based on model outputs. Probability distribution functions of input variables were determined based on field-measured data obtained under alternative tillage treatments. Uncertainty of model outputs is investigated using a Latin hypercube sampling scheme (LHS) with restricted pairing for model input sampling. Probability density functions (pdfs) are constructed for water flow rate, atrazine leaching rate, total accumulated leaching, and atrazine concentration in percolation water. Results indicate that consideration of input parameter uncertainty produces a 20% higher mean flow rate along with two to three times larger atrazine leaching rate, accumulated leachate, and concentration than that obtained using mean input parameters. Uncertainty in predicted flow rate is small but that in solute transport is an order of magnitude larger than that of corresponding input parameters. Macropore flow is observed to contribute to the variability of atrazine transport results. Overall, the analysis provides a quantification of prediction uncertainty that is found to enhance a user's ability to assess risk levels associated with model predictions.     

One-at-a-time sensitivity analysis of pollutant loadings to subsurface properties for the assessment of soil and groundwater pollution potential

Chemical leak was numerically simulated for four chemical substances: benzene (light non-aqueous phase liquid (NAPL)), tetrachloroethylene (dense NAPL), phenol (soluble in water), and pentachlorophenol (white crystalline solid) in a hypothetical subsurface leak situation using a multiphase compositional transport model. One metric ton of chemical substances was assumed to leak at a point 3.51 m above the water table in a homogeneous unconfined aquifer which had the depth to water table of 7.135 m, the hydraulic gradient of 0.00097, the recharge rate of 0.7 mm/day, and the permeability of 2.92?×?10^?10 m². For comparison, surface spill scenarios, which had a long pathway from source to the water table, were simulated. Using the model results, point-source pollutant loadings to soil and groundwater were calculated by multiplying mass, impact area, and duration above and below the water table respectively. Their sensitivity to subsurface properties (depth to water table, recharge rate, porosity, organic carbon content, decay rate, hydraulic gradient, capillary pressure, relative permeability, permeability) was analyzed, with changing each parameter within acceptable ranges. The study result showed that the pollutant loading to groundwater was more sensitive to the subsurface properties than the pollutant loading to soil. Decay rate, groundwater depth, hydraulic gradient and porosity were influential to pollutant loadings. The impact of influential parameters on pollutant loadings was nonlinear. The dominant subsurface properties of pollution loadings (e.g., decay rate, groundwater depth, hydraulic gradient, and porosity for groundwater) also affect the vulnerability, and the subsurface pollutant loadings defined in this study are dependent on chemical properties as well, which indicates that the influential hydrogeological and physicochemical parameters to pollutant loadings can be used for pollution potential assessment. The contribution of this work is the suggestion that the sensitivity of pollutant loadings can be used for pollution potential assessment. Soil and groundwater pollution potential of chemicals are discussed altogether for leak scenarios. A physics-based model is used to understand the impact of subsurface properties on the fate and transport of chemicals above and below the water table, and consequently their impact on the pollutant loading to soil and groundwater.

     

Global sensitivity analysis of a process-based model for ammonia emissions from manure storage and treatment structures

The development of process-based models to estimate ammonia emissions from animal feeding operations (AFOSs) is sought to replace costly and time-consuming direct measurements. Critical to process-based model development is conducting sensitivity analysis to determine the input parameters and their interactions that contribute most to the variance of the model output. Global and relative sensitivity analyses were applied to a process-based model for predicting ammonia emissions from the surface of anaerobic lagoons for treating and storing manure. The objectives were to compare global sensitivity analysis (GSA) to relative (local) sensitivity analysis (RSA) on a process-based model for ammonia emissions. Based on the first-order coefficient, both GSA and RSA showed the model input parameters in order of importance in process model for ammonia emissions from lagoon surfaces were: (i) pH, (ii) lagoon liquid temperature, (iii) wind speed above the lagoon surface, and (iv) the concentration of ammoniacal nitrogen in the lagoon. The GSA revealed that interactions between model parameters accounted for over two-thirds of the model variance, a result that cannot be achieved using traditional RSA. Also, the GSA showed that parameter interactions involving liquid pH had more impact on the model output variance than the single parameters: (i) temperature, (ii) wind speed, or (iii) total ammoniacal nitrogen. This study demonstrates that GSA provides a more complete analysis of model input parameters and their interactions on the model output compared to RSA. A comprehensive tutorial regarding the application of GSA to a process model is presented.     

Impacts of swine manure pits on groundwater quality

Manure deep-pits are commonly used to store manure at confined animal feeding operations. However, previous to this study little information had been collected on the impacts of deep-pits on groundwater quality to provide science-based guidance in formulating regulations and waste management strategies that address risks to human health and the environment. Groundwater quality has been monitored since January 1999 at two hog finishing facilities in Illinois that use deep-pit systems for manure storage. Groundwater samples were collected on a monthly basis and analyzed for inorganic and bacteriological constituent concentrations. The two sites are located in areas with geologic environments representing different vulnerabilities for local groundwater contamination. One site is underlain by more than 6 m of clayey silt, and 7-36 m of shale. Concentrations of chloride, ammonium, phosphate, and potassium indicated that local groundwater quality had not been significantly impacted by pit leakage from this facility. Nitrate concentrations were elevated near the pit, often exceeding the 10 mg N/l drinking water standard. Isotopic nitrate signatures suggested that the nitrate was likely derived from soil organic matter and fertilizer applied to adjacent crop fields. At the other site, sandstone is located 4.6-6.1 m below land surface. Chloride concentrations and delta15N and delta15O values of dissolved nitrate indicated that this facility may have limited and localized impacts on groundwater. Other constituents, including ammonia, potassium, phosphate, and sodium were generally at or less than background concentrations. Trace- and heavy-metal concentrations in groundwater samples collected from both facilities were at concentrations less than drinking water standards. The concentration of inorganic constituents in the groundwater would not likely impact human health. Fecal streptococcus bacteria were detected at least once in groundwater from all monitoring wells at both sites. Fecal streptococcus was more common and at greater concentrations than fecal coliform. The microbiological data suggest that filtration of bacteria by soils may not be as effective as commonly assumed. The presence of fecal bacteria in the shallow groundwater may pose a significant threat to human health if the ground water is used for drinking. Both facilities are less than 4 years old and the short-term impacts of these manure storage facilities on groundwater quality have been limited. Continued monitoring of these facilities will determine if they have a long-term impact on groundwater resources.     

Effects of land use and land cover, stream discharge, and interannual climate on the magnitude and timing of nitrogen, phosphorus, and organic carbon concentrations in three coastal plain watersheds.

In-stream nitrogen, phosphorus, organic carbon, and suspended sediment concentrations were measured in 18 subbasins over 2 annual cycles to assess how land use and land cover (LULC) and stream discharge regulate water quality variables. The LULC was a primary driver of in-stream constituent concentrations and nutrient speciation owing to differences in dominant sources and input pathways associated with agricultural, urban, and forested land uses. Stream discharge was shown to be a major factor that dictated not only the magnitude of constituent concentrations, but also the chemical form. In high discharge agricultural subbasins, where nitrate was the dominant nitrogen form, there was a negative correlation between discharge and nitrate concentration indicating groundwater inputs as the dominant pathway. In urban settings, however, nitrate was positively correlated with discharge, and, in forested subwatersheds, where dissolved organic nitrogen (DON) was the dominant nitrogen form, there was a positive correlation between discharge and DON, indicating washoff from the watershed as the dominant input pathway. Similarly, phosphorus concentrations were strongly regulated by LULC, discharge, and seasonality. This comparative study highlights that different mechanisms regulate different forms of nitrogen, phosphorus, and carbon, and thus field programs or water quality models used for regulatory purposes must assess these nutrient forms to accurately apply management plans for nutrient reductions.     

Climate change impacts on the leaching of a heavy metal contamination in a small lowland catchment

The Keersop catchment (43km(2)) in the south of The Netherlands has been contaminated by the emissions of four zinc ore smelters. The objective of this study was to assess the effects of future projected climate change on the hydrology and the leaching of heavy metals (i.e. Cd and Zn) in the catchment. The numerical, quasi-2D, unsaturated zone Soil Water Atmosphere Plant model was used with 100-year simulated daily time series of precipitation and potential evapotranspiration. The time series are representative of stationary climates for the periods 1961-1990 ("baseline") and 2071-2100 ("future"). The time series of future climate were obtained by downscaling the results of eight regional climate model (RCM) experiments, driven by the SRES A2 emissions scenario, using change factors for a series of climate statistics and applying them to stochastic weather generator models. The time series are characterized by increased precipitation in winter, less precipitation in summer, and higher air temperatures (between 2°C and 5°C) throughout the year. Future climate scenarios project higher evapotranspiration rates, more irrigation, less drainage, lower discharge rates and lower groundwater levels, due to increased evapotranspiration and a slowing down of the groundwater system. As a result, lower concentrations of Cd and Zn in surface water are projected. The reduced leaching of heavy metals, due to drying of the catchment, showed a positive impact on a limited aspect of surface water quality.     

Contaminant transport in a municipal drinking water supply: a steady-state approach to estimate rate and uncertainty

Contaminant transport is generally considered to be a key factor when assessing and classifying the environmental risk of polluted areas. In the study presented here, a steady-state approach was applied to obtain estimates of the transit time and concentration of the pesticide metabolite BAM (2,6-dichlorobenzoamide) at a site where it is contaminating a municipal drinking water supply. A Monte Carlo simulation technique was used to quantify the uncertainty of the results and to evaluate the sensitivity of the used parameters. The adopted approach yielded an estimated median transit time of 10 y for the BAM transport from the polluted site to the water supply. Soil organic carbon content in the unsaturated zone and the hydraulic conductivity in the saturated zone explained 44% and 23% of the uncertainty in the transit time estimate, respectively. The sensitivity analysis showed that the dilution factor due to regional groundwater flow and the soil organic carbon content at the polluted site explained 53% and 31% of the uncertainty of concentration estimates, respectively. In conclusion, the adopted steady-state approach can be used to obtain reliable first estimates of transit time and concentration, but to improve concentration predictions of degrading contaminants, a dynamic model is probably required.     

The groundwater contribution to surface water contamination in a region with intensive agricultural land use (Noord-Brabant, The Netherlands)

Traditionally, monitoring of soil, groundwater and surface water quality is coordinated by different authorities in the Netherlands. Nowadays, the European Water Framework Directive (EU, 2,000) stimulates an integrated approach of the complete soil-groundwater-surface water system. Based on water quality data from several test catchments, we propose a conceptual model stating that stream water quality at different discharges is the result of different mixing ratios of groundwater from different depths. This concept is used for a regional study of the groundwater contribution to surface water contamination in the Dutch province of Noord-Brabant, using the large amount of available data from the regional monitoring networks. The results show that groundwater is a dominant source of surface water contamination. The poor chemical condition of upper and shallow groundwater leads to exceedance of the quality standards in receiving surface waters, especially during quick flow periods.     

Field calibration of surface: A model of agricultural chemicals in surface waters

Abstract

Agricultural chemicals sporadically occur at detectable levels in the surface waters of intensively farmed watersheds. HSPF, a previously released model of agricultural chemicals in surface water, had been used to predict concentrations which were much higher (10 X) than those actually observed during monitoring studies. A new model, SURFACE, is described here which is much simpler than HSPF and gives better predictions of surface water concentrations. SURFACE uses PRZM, an EPA model, to calculate edge‐of‐field runoff losses and simple hydraulic routing algorithms to determine concentrations at the bottom of large river basins. In water systems sampled during 1985 and 1986, SURFACE predictions of annualized mean concentrations for alachlor, atrazine, cyanazine and metolachlor were within 0.09 ppb half of the time.     

Air and groundwater pollution in an agricultural region of the Turkish Mediterranean coast

Air pollution and groundwater pollution in conjunction with agricultural activity were investigated in Antayla province on the Turkish Mediterranean coast. The air pollution was investigated in terms of gas-phase nitric acid (HNO3), sulfur dioxide (SO2), ammonia (NH3), and particulate matter for a 6-month period in the atmosphere using a "filter pack" system, which was developed and optimized in our laboratory. Ozone was measured by using an automated analyzer. Among all of the gas-phase pollutants, HNO3 had the lowest concentration (0.42 microg x m(-3)) followed by NH3. Agricultural activities seem to be the major source of observed NH3 in the air. The current state of water pollution was investigated in terms of organochlorine and organophosphorus pesticides around the greenhouses, in which mainly tomato, pepper, and eggplant are cultivated. Water samples were collected from 40 points, 28 of which were wells and 12 of which were surface water. The pesticide concentrations in water samples were determined by means of solid-phase extraction (SPE) followed by a gas chromatography (GC)-electron capture detector (ECD)/nitrogen phosphorus detector (NPD) system. In general, surface water samples were more polluted by the pesticides than groundwater samples. The most frequently observed pesticides were chlorpyriphos (57%) and aldrin (79%) in groundwater, and chlorpyriphos (75%), aldrin, and endosulfan sulfate (83%) in surface water samples. The highest concentrations were observed for fenamiphos (394.8 ng/L) and aldrin (68.51 ng/L) in groundwater, and dichlorvos (322.2 ng/L) and endosulfan sulfate (89.5 ng/L) in surface water samples. At least one pesticide had a concentration above the health limit in 38% of all the water samples analyzed.