Net fluxes of pesticides from the Scheldt Estuary into the North Sea: a model approach

A hydrological model was constructed with the commercially available modelling environment ECoS for the calculation of mixing plots of dissolved pesticide concentrations in the Scheldt Estuary. The model was based on a one-dimensional advection-dispersion equation and dispersion coefficients were calculated from measured salinity profiles. The model could correctly predict the movement of water masses within the estuary. Nominal flushing times of the estuary were calculated with the fraction-of-freshwater method and ranged between 25 and 95 days, depending on the freshwater discharge. Model calculations demonstrated that the application-related time profiles of pesticide input may well induce curvature in the calculated estuarine mixing plot even if the pesticide behaves conservatively. Calculated mixing plots were compared with experimental data and good agreement was obtained between the measurements and the conservative mixing plot of atrazine. For metolachlor an additional direct emission had to be modelled to explain the differences between the experimental data and the conservative mixing plot. For dichlorvos and simazine. on the other hand, an estuarine loss constant had to be included in the model. Using a least-squares procedure the estuarine loss constants for dichlorvos and simazine were estimated at half-lives of five and 26 days, respectively. Because mixing plots were strongly influenced by the time profiles of pesticide input, standard procedures in which net fluxes are calculated from mixing plots, were not applicable. Therefore, net fluxes were calculated with a newly developed procedure in which the estuarine loss constants and the estuarine flushing time were combined. For the non-conservative compounds dichlorvos and simazine, the net fluxes to the North Sea were found to be 96 and 64%, respectively, lower than the gross fluxes transported by the River Scheldt.     

Modelling assessment of regional groundwater contamination due to historic smelter emissions of heavy metals

Historic emissions from ore smelters typically cause regional soil contamination. We developed a modelling approach to assess the impact of such contamination on groundwater and surface water load, coupling unsaturated zone leaching modelling with 3D groundwater transport modelling. Both historic and predictive modelling were performed, using a mass balance approach for three different catchments in the vicinity of three smelters. The catchments differ in their hydrology and geochemistry. The historic modelling results indicate that leaching to groundwater is spatially very heterogeneous due to variation in soil characteristics, in particular soil pH. In the saturated zone, cadmium is becoming strongly retarded due to strong sorption at neutral pH, even though the reactivity of the sandy sediments is low. A comparison between two datasets (from 1990 to 2002) on shallow groundwater and modelled concentrations provided a useful verification on the level of statistics of "homogeneous areas" (areas with comparable land use, soil type and geohydrological situation) instead of comparison at individual locations. While at individual locations observations and the model varies up to two orders of magnitude, for homogeneous areas, medians and ranges of measured concentrations and the model results are similar. A sensitivity analysis on metal input loads, groundwater composition and sediment geochemistry reveals that the best available information scenario based on the median value of input parameters for the model predicts the range in observed concentrations very well. However, the model results are sensitive to the sediment contents of the reactive components (organic matter, clay minerals and iron oxides). Uncertainty in metal input loads and groundwater chemistry are of lesser importance. Predictive modelling reveals a remarkable difference in geochemical and hydrological controls on subsurface metal transport at catchment-scale. Whether the surface water load will peak within a few decades or continue to increase until after 2050 depends on the dominant land use functions in the areas, their hydrology and geochemical build-up.     

Concentration of LAS and boron in the Itter. Comparison of measured data with results obtained by simulation with the GREAT-ER software

The GREAT-ER software was used to calculate linear alkylbenzene sulphonate [LAS] and boron concentrations in the Itter stream in North Rhine-Westfalia, Germany. The aim was to investigate the predictive strength of this newly developed tool and to compare its results to measured data. Substance-specific input data which were used in this scenario were partly generic (e.g. LAS and sodium perborate tetrahydrate consumption figures for Germany) and partly (site-)specific data (e.g. half-life time for LAS elimination). The comparison with the measured data reveals that the model predictions for LAS and boron are correct at least within a factor of two, only if generic German consumption data is applied. By using refined input data, the accuracy can be increased further.     

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.     

Directional biases in back trajectories caused by model and input data

Back trajectory analyses are often used for source attribution estimates in visibility and other air quality studies. Several models and gridded meteorological datasets are readily available for generation of trajectories. The Big Bend Regional Aerosol and Visibility Observational (BRAVO) tracer study of July to October 1999 provided an opportunity to evaluate trajectory methods and input data against tracer concentrations, particulate data, and other source attribution techniques. Results showed evidence of systematic biases between the results of different back trajectory model and meteorological input data combinations at Big Bend National Park during the BRAVO. Most of the differences were because of the choice of meteorological data used as input to the trajectory models. Different back trajectories also resulted from the choice of trajectory model, primarily because of the different mechanisms used for vertical placement of the trajectories. No single model or single meteorological data set was found to be superior to the others, although rawinsonde data alone are too sparse in this region to be used as the only input data, and some combinations of model and input data could not be used to reproduce known attributions of tracers and simulated sulfate.     

Flow rates of ions in waters percolating through a model ecosystem with forest trees

From 1983-88 the long-term effects of low level exposure with O(3), SO(2) and simulated acid rain on mineral cycling in model ecosystems with spruce, fir and beech seedlings were investigated. Systems consisting of open-top chambers built above lysimeters were protected against the intrusion of ambient rain and dust. As part of the investigations on mineral cycling the fluxes of elements with water input and output of the canopy and soil compartments are presented. During the 5 year duration of the experiment, pronounced effects on canopy deposition and cation leaching were observed. Most noticeable were throughfall enrichment with sulfate through dry deposition of SO(2) as influenced by duration of needlewetting and factors promoting SO(2) oxidation. Depending on sulfur deposition, leaching of calcium, magnesium, manganese, zinc and ammonium from canopies was elevated, in total leading to enhanced soil input of acid. After 15 months, the water percolating the soils in the lysimeters of these treatments was acidified, with elevated flowrates of sulfate, manganese, calcium and magnesium. The results on canopy/soil leaching are compared to those from old conifer stands in the field.     

Artificial neural network for the identification of unknown air pollution sources

Artificial neural networks (ANN), whose performances to deal with pattern recognition problems is well known, are proposed to identify air pollution sources. The problem that is addressed is the apportionment of a small number of sources from a data set of ambient concentrations of a given pollutant. Three layers feed-forward ANN trained with a back-propagation algorithm are selected. A test case is built, based on a Gaussian dispersion model. A subset of hourly meteorological conditions and measured concentrations constitute the input patterns to the network that is wired to recover relevant emission parameters of unknown sources as outputs. The rest of the model data are corrupted adding noise to some meteorological parameters and we test the effectiveness of the method to recover the correct answer. The ANN is applied to a realistic case where 24 h SO₂ concentrations were previously measured. Some of the limitations of the ANN approach, together with its capabilities, are discussed in this paper.     

The dependence of airborne particulate deposition on atmospheric stability and surface conditions

The deposition of airborne Pb particulates has been experimentally investigated in different fields. Three fields with different surface conditions were selected for which the roughness sizes have been determined under neutral atmospheric conditions. A modified low-pressure Andersen impactor was used to determine the particle-size distribution. The sampler classifies particle size in ultra-fine range down to 0.08 μm aerodynamic diameter. The flux of particulate Pb was measured on the surfaces of: alfalfa leaves, real grass and soil under stable and unstable atmospheric conditions.Data from a meteorological tower were analysed to determine the standard deviation of horizontal wind direction from which the atmospheric stability class was determined. The deposition surfaces were wet-ashed with HNO₃ and analysed for Pb by atomic absorption spectrophotometry and the particulate fluxes were obtained. The particulate fluxes were also calculated from the size-distribution data and values of deposition velocity were obtained from two deposition models (Sehmel and Hodgson, 1978; El-Shobokshy and Sharaf El-Din, 1984).It is concluded that both surface roughness and atmospheric stability are the main controlling factors in particulate deposition, together with particle diameter. For unstable conditions, there was quite good agreement between measured and calculated particulate flux using both of the above theoretical models. For stable conditions, Sehmel and Hodgson's model failed to predict accurate fluxes because their model shows the independence of deposition velocity on atmospheric stability.     

Heavy metal budgets for two headwater forested catchments in background areas of Finland.

Mean annual (1994-1996) budgets for Cd, Cu, Ni, Pb and Zn at two background, forested catchments, VK and HJ, in Finland are presented. Budgets for plots (VK3, HJ1 and HJ4) included throughfall (TF), litterfall (LF) and soil leaching fluxes, and for catchments terrestrial retention and leaching and lake sedimentation fluxes. Total deposition (TD) loads were relatively low (Cd < 0.1, Cu < 2, Ni < 1, Pb < 3 and Zn < 5 mg m-2 year-1) and that even in these areas almost half of the TD was in the form of dry deposition. Retention of TD within catchments was > or = 77% for all metals, except for Ni at VK (54%). For Cu and Pb, the retention was 94-97%. Most of the retention (74-97%) took place in the terrestrial part of the catchment, lake sedimentation accounting for the remainder. Plot-scale soil leaching fluxes at 40 cm of Cd, Cu (VK3) and Ni (VK3) were greater (> or = 100%) than TD inputs. Most of the catchment retention must therefore have taken place either deeper in the soil or in the lowland peatland areas. The humus layer was particularly effective in retaining Cu and Cd (65-81% and 51-78% of total inputs to the forest floor (TF + LF)). The retention of Pb by the humus layer was less than expected (26-54% of TF + LF). Litterfall was a particularly important internal flux for Zn.     

Neural network model for predicting peak photochemical pollutant levels

In this paper, an attempt is made for the 24-hr prediction of photochemical pollutant levels using a neural network model. For this purpose, a model is developed that relates peak pollutant concentrations to meteorological and emission variables and indexes. The analysis is based on measurements of O3 and NO2 from the city of Athens. The meteorological variables are selected to cover atmospheric processes that determine the fate of the airborne pollutants while special care is taken to ensure the availability of the required input data from routine observations or forecasts. The comparison between model predictions and actual observations shows a good agreement. In addition, a series of sensitivity tests is performed in order to evaluate the sensitivity of the model to the uncertainty in meteorological variables. Model forecasts are generally rather insensitive to small perturbations in most of the input meteorological data, while they are relatively more sensitive in changes in wind speed and direction.     

Manipulation of precipitation in small headwater catchments at Storgama, Norway: effects on leaching of organic carbon and nitrogen species

Projected changes in climate in Southern Norway include increases in summer and autumn precipitation. This may affect leaching of dissolved organic matter (DOM) from soils. Effects of experimentally added extra precipitation (10 mm week) during the growing season of 3 years (2004-2006) to small headwater catchments at Storgama (59 degrees 0'N, 550-600 m a.s.l.) on leaching of total organic carbon (TOC) and total organic nitrogen (TON) were assessed. Extra precipitation did not have a significant effect on average TOC and TON concentrations in runoff. Thus, fluxes of TOC and TON increased nearly proportionally with water fluxes. This suggests that a store of adsorbed and potentially mobile TOC and TON in catchment soils buffers the concentration of DOM in runoff. The size and dynamics of the pool of TOC and TON depends on the balance between production and leaching rates. Infrequent short droughts had only small effects on TOC and TON fluxes in runoff from the reference catchments.     

NLEAP model simulation of climate and management effects on N leaching for corn grown on sandy soil

The Nitrate Leaching and Economic Analysis Package (NLEAP) model was used to evaluate effects of climate and N fertility on nitrate leaching from a 3-yr field experiment of continuous corn (Zea mays L.). Half of the plots were randomly chosen to be either nonirrigated or irrigated (based upon calculated potential evapotranspiration). Three replications of nitrogen (N) fertility (56, 112 and 224 kg ha⁻¹) were used. Soil was a Hecla sandy loam to loamy sand (Pachic Udic Haploboroll). Soil and climate data were from the upper Midwest U.S.A. database for NLEAP. On-site data were used in the model when available.This study shows that NLEAP is capable of integrating data collected for nonirrigated and irrigated conditions on sandy soil for a wide range of N treatments and predicting the nitrate available for leaching (NAL). Precipitation distribution and amount were different in each year. Calculated NAL provided an excellent indicator of potential nitrate leaching hazard. NLEAP output showed that leaching of residual N on this sandy soil is very sensitive to early-spring precipitation. The NLEAP model provided valuable insights concerning effects of climate and N and irrigation management on N leaching. To obtain optimum yields while minimizing nitrate leaching, this study indicates the need to use soil and plant-tissue testing, post-emergence N-fertilizer application, and modem irrigation-scheduling technology. Also, use of the NLEAP model along with field-plot experiments provide additional important information concerning timing of N-leaching events relative to climate and an additional assessment of the effectiveness of fertilizer-N management decisions.     

Fluxes of inorganic and organic arsenic species in a Norway spruce forest floor

To identify the role of the forest floor in arsenic (As) biogeochemistry, concentrations and fluxes of inorganic and organic As in throughfall, litterfall and forest floor percolates at different layers were investigated. Nearly 40% of total As(total) input (5.3g Asha(-1)yr(-1)) was retained in Oi layer, whereas As(total) fluxes from Oe and Oa layers exceeded the input by far (10.8 and 20g Asha(-1)yr(-1), respectively). Except dimethylarsinic acid (DMA), fluxes of organic As decreased with depth of forest floor so that <10% of total deposition (all <0.3g Asha(-1)yr(-1)) reached the mineral soil. All forest floor layers are sinks for most organic As. Conversely, Oe and Oa layers are sources of As(total), arsenite, arsenate and DMA. Significant correlations (r>/=0.43) between fluxes of As(total), arsenite, arsenate or DMA and water indicate hydrological conditions and adsorption-desorption as factors influencing their release from the forest floor. The higher net release of arsenite from Oe and Oa and of DMA from Oa layer in the growing than dormant season also suggests microbial influences on the release of arsenite and DMA.     

Throughfall below grassland canopies: a comparison of conventional and ion exchange methods

Sampling of canopy fluxes (throughfall and stemflow) below low structured vegetation with a small-scale, intricate canopy architecture is difficult, and representative sampling with most methods is questionable. In the present study, two sampling methods for canopy fluxes below grassland vegetation are compared. Method I sampled canopy fluxes of moisture inefficiently, because stemflow volumes were not quantitatively included. Canopy fluxes of ions calculated with method I necessitated assumptions on equal concentrations in actually sampled throughfall and non-sampled stemflow. Method II sampled canopy fluxes of ions quantitatively, because the total volume of throughfall and stemflow percolated through a mixed bed of ion exchange resins below the canopy. Ion-specific differences between the two methods were observed. For ions with foliar leaching, such as K+ and Ca2+, higher canopy fluxes were recorded with method II than with method I. In contrast, for ions with foliar uptake, such as NH4+ and NO3-, canopy fluxes were found to be less with method II than with method I. Canopy fluxes of inorganic nitrogen below Mesobrometum grassland were 2.35 and 1.52 kmol(c) ha(-1) year(-1) for methods I and II, respectively, and 2.85 and 7.90 kmol(c) ha(-1) year(-1) for K+. It is argued that these differences result from under-estimated (foliar leaching) or over-estimated (foliar uptake) concentrations in stemflow by the first method. Canopy fluxes for SO4(2-) were not statistically different, indicating that canopy exchange of SOx was quantitatively unimportant, and that both methods estimated atmospheric input equally well.     

Controls on atrazine leaching through a soil-unsaturated fractured limestone sequence at Brévilles, France

The objective of this study was to identify the main controls on atrazine leaching through luvisols and calcisols overlying fissured limestone using the dual-permeability model MACRO. The model parameterisation was based on a combination of direct measurements (e.g. hydraulic properties, adsorption and degradation), literature data and calibration against bromide leaching experiments in field plots. A Monte Carlo sensitivity analysis was carried out for a typical application pattern, considering two different depths of unsaturated limestone (15 and 30 m). MACRO calibrations to the field experiments demonstrated the occurrence of strong macropore flow in the luvisol, while transport in the calcisol could be described by the advection-dispersion equation. MACRO simulations of tritium and atrazine leaching qualitatively matched tritium concentration profiles measured in the limestone and atrazine concentrations measured in piezometers and in aquifer discharge via a spring. The sensitivity analysis suggested that the thickness of the limestone, as well as the transport properties and processes occurring in the unsaturated rock (e.g. matrix vs. fissure flow) will have little significant long-term effect on atrazine leaching, mainly because degradation is very slow in the limestone. No mineralization of atrazine was detected in one-year incubations and a mean half-life of 10 years was assumed in the simulations. Instead, processes occurring in the soil exerted the main control on predicted atrazine leaching, especially variations in the degradation rate and the strength of sorption and macropore flow. However, fissure flow in unsaturated rock is expected to exert a much more significant control on groundwater contamination for compounds that degrade more readily in the deep vadose zone.     

Testing the importance of accurate meteorological input fields and parameterizations in atmospheric transport modelling using dream - validation against ETEX-1

A tracer model, the DREAM, which is based on a combination of a near-range Lagrangian model and a long-range Eulerian model, has been developed. The meteorological meso-scale model, MM5V1, is implemented as a meteorological driver for the tracer model. The model system is used for studying transport and dispersion of air pollutants caused by a single but strong source as, e.g. an accidental release from a nuclear power plant. The model system including the coupling of the Lagrangian model with the Eulerian model are described. Various simple and comprehensive parameterizations of the mixing height, the vertical dispersion, and different meterological input data have been implemented in the combined tracer model, and the model results have been validated against measurements from the ETEX-1 release. Several different statistical parameters have been used to estimate the differences between the parameterizations and meterological input data in order to find the best performing solution.     

Neural Network Model for Predicting Peak Photochemical Pollutant Levels

ABSTRACT

In this paper, an attempt is made for the 24-hr prediction of photochemical pollutant levels using a neural network model. For this purpose, a model is developed that relates peak pollutant concentrations to meteorological and emission variables and indexes. The analysis is based on measurements of O₃ and NO₂ from the city of Athens. The meteorological variables are selected to cover atmospheric processes that determine the fate of the airborne pollutants while special care is taken to ensure the availability of the required input data from routine observations or forecasts. The comparison between model predictions and actual observations shows a good agreement. In addition, a series of sensitivity tests is performed in order to evaluate the sensitivity of the model to the uncertainty in meteorological variables. Model forecasts are generally rather insensitive to small perturbations in most of the input meteorological data, while they are relatively more sensitive in changes in wind speed and direction.     

Prediction of maximum of 24-h average of PM10 concentrations 30 h in advance in Santiago,Chile

We have developed a neural network based model that uses values of PM10 concentrations measured until 6 p.m. on the present day plus measured and forecasted values of meteorological variables as input in order to predict the level reached by the maximum of the 24-h moving average (24MA) of PM10 concentration on the next day. We have adjusted the parameters of the model using 1998 data to predict 1999 conditions and 1999 data to forecast 2000 maximum concentrations. We have found that among the relevant meteorological input variables, the forecasted difference between maximum and minimum temperature is the most important. Due to the fact that local authorities impose restrictions to emissions on days when the maximum of 24MA of PM10 concentration is expected to exceed 240 μg/m³, we have corrected the measured concentrations on these days before evaluating the efficacy of the forecasting model. Percent errors in forecasting the numerical value are of the order of 20%. The performance of the neural network is better than that of a linear model with the same inputs, but the difference being not great is an indication that the right choice of input variables may be more important than the decision to use a linear or a nonlinear model.