Modelling biocide and herbicide concentrations in catchments of the Rhine basin

Impairment of water quality by organic micropollutants such as pesticides, pharmaceuticals or household chemicals is a problem in many catchments worldwide. These chemicals originate from different urban and agricultural usages and are transferred to surface waters from point or diffuse sources by a number of transport pathways. The quantification of this form of pollution in streams is challenging and especially demanding for diffuse pollution due to the high spatio-temporal concentration dynamics, which require large sampling and analytical efforts to obtain representative data on the actual water quality.Models can also be used to predict to what degree streams are affected by these pollutants. However, spatially distributed modelling of water quality is challenging for a number of reasons. Key issues are the lack of such models that incorporate both urban and agricultural sources of organic micropollutants, the large number of parameters to be estimated for many available water quality models, and the difficulty to transfer parameter estimates from calibration sites to areas where predictions are needed.To overcome these difficulties, we used the parsimonious iWaQa model that simulates herbicide transport from agricultural fields and diffuse biocide losses from urban areas (mainly façades and roof materials) and tested its predictive capabilities in the Rhine River basin. The model only requires between one and eight global model parameters per compound that need to be calibrated. Most of the data requirements relate to spatially distributed land use and comprehensive time series of precipitation, air temperature and spatial data on discharge. For larger catchments, routing was explicitly considered by coupling the iWaQa to the AQUASIM model.The model was calibrated with datasets from three different small catchments (0.5–24.6&thinsp;km2) for three agricultural herbicides (isoproturon, S-metolachlor, terbuthylazine) and two urban biocides (carbendazim, diuron). Subsequently, it was validated for herbicides and biocides in Switzerland for different years on 12 catchments of much larger size (31–35&thinsp;899&thinsp;km2) and for herbicides for the entire Rhine basin upstream of the Dutch–German border (160&thinsp;000&thinsp;km2) without any modification. For most compound–catchment combinations, the model predictions revealed a satisfactory correlation (median r2: 0.5) with the observations. The peak concentrations were mostly predicted within a factor of 2 to 4 (median: 2.1 fold difference for herbicides and 3.2 for biocides respectively). The seasonality of the peak concentration was also well simulated; the predictions of the actual timing of peak concentrations, however, was generally poor.Limited spatio-temporal data, first on the use of the selected pesticides and second on their concentrations in the river network, restrict the possibilities to scrutinize model performance. Nevertheless, the results strongly suggest that input data and model structure are major sources of predictive uncertainty. The latter is for example seen in background concentrations that are systematically overestimated in certain regions, which is most probably linked to the modelled coupling of background concentrations to land use intensity.Despite these limitations the findings indicate that key drivers and processes are reasonably well approximated by the model and that such a simple model that includes land use as a proxy for compound use, weather data for the timing of herbicide applications and discharge or precipitation as drivers for transport is sufficient to predict the timing and level of peak concentrations within a factor of 2 to 3 in a spatially distributed manner at the scale of large river basins.</p

A Modeling Approach to Understanding Glyphosate Transport in the Belize River Watershed

Glyphosate is the most widely used herbicide worldwide and is often transported from application areas to surface water when solubilized in runoff or sorbed to eroded sediment. There is evidence that suggests both glyphosate and its main metabolite aminomethylphosphonic acid (AMPA) may pose a risk to human health, as well as cause adverse effects in the environment. However, consistent monitoring data is still limited, especially in developing countries. Belize is a developing nation with agriculture being a major sector of its economy and is heavily reliant on glyphosate. The widespread use of glyphosate in Belize may be resulting in glyphosate transport to drinking water resources. Samples were collected from two rural communities that rely on the Belize River for their drinking water systems, Bullet Tree and Spanish Lookout, at points upstream of the abstraction site, at the abstraction site, and at the site of drinking water distribution. Samples were analyzed using HPLC, ELISA kits, and LC-MS/MS. From these analyses, it was concluded that glyphosate was not present in any water samples at a detectable concentration. The Soil and Water Assessment Tool (SWAT) was used to develop a model of the Belize River Watershed. The model was calibrated and validated for observed flow rates using the SWAT Calibration and Uncertainty Program (SWAT-CUP), which revealed acceptable model performance for simulating flow. Model results indicate that glyphosate transport to the Belize River is occurring, with contributions from glyphosate sorbed to eroded sediment being significantly greater than soluble glyphosate in surface runoff (p-values \u3c0.0). Average simulated concentrations of soluble glyphosate in both wet and dry seasons are below the European Union (EU) standard of 0.1 ppb across the watershed. However, subbasins 2, 3, and 28 were identified as higher risk areas, due to having the highest percentages of days exceeding the EU standard. Subbasin 28, located just downstream of the Spanish Lookout drinking water system, was the most significant contributor of soluble glyphosate to the river, as compared to soluble glyphosate concentrations in subbasins 2 (p-values \u3c0.0) and 3 (p-values \u3c0.0). Soluble glyphosate concentrations in subbasin 28 inflow and outflow exceeded the EU standard 12.53% and 11.65% of the time, respectively. This work demonstrates a framework for applying SWAT for pesticide transport modeling in developing countries, and has the potential to be a powerful and accessible tool for watershed management when monitoring data is unavailable

Aplicación del proceso de coagulación mejorada para la remoción de atrazina utilizando como coagulante hidroxicloruro de aluminio

La atrazina es un plaguicida ampliamente utilizado en el mundo, que afecta la salud humana y el ambiente. El proceso de clarificación convencional del agua no remueve significativamente estas sustancias, mientras que la coagulación mejorada ha demostrado buenos resultados en la eliminación de materia orgánica natural. En este estudio se evaluó a escala de laboratorio, el proceso de coagulación mejorada en la remoción de tres concentraciones de atrazina, empleando como coagulante Hidroxicloruro de Aluminio (ACH). Se realizó la caracterización de la fuente de agua utilizada para evidenciar la presencia de este plaguicida, y una revisión sistemática de literatura para determinar las concentraciones máximas de atrazina presentes en cuerpos de agua superficial a nivel mundial (Max. 250 µg/L). A partir de esta revisión, se seleccionaron las concentraciones de atrazina a dopar 500, 1.000 y 1.500 µg/L. Por medio de diagramas de coagulación, se evaluó de manera indirecta la eficiencia de remoción de atrazina, en términos de absorbancia UV223, y la remoción de turbiedad y color verdadero. El proceso de coagulación mejorada con ACH redujo en un 90 % la turbidez y el color verdadero presentes en el agua. Las mejores remociones de atrazina, en términos de absorbancia UV223, se lograron disminuyendo el pH inicial del agua (4,0 - 10 mg/L ACH - pH: 4,4 - 5,0 UND), alcanzando remociones máximas del 55 %, 30 % y 30 % para las tres concentraciones iniciales de atrazina 500 µg/L, 1.000 µg/L y 1.500 µg/L respectivamente. La aplicación de coagulante en exceso del requerido (30 - 65 mg/L ACH - pH: 5,5 - 6,5 UND) presentó buenos resultados en la remoción de absorbancia UV223, sin embargo, comprometió la calidad del agua en términos de turbiedad, por lo tanto, las mejores opciones desde el punto de vista operativo para remover atrazina, turbiedad y color verdadero correspondieron al aumento del pH del agua (4,0 - 10 mg/L ACH - pH: 7,0 - 7,8 UND) o la aplicación de coagulación convencional (4,0 - 16 mg/L ACH).PregradoINGENIERO(A) SANITARIA Y AMBIENTA

GIS-based modelling of agrochemical use, distribution and accumulation in the Lower Mekong Delta, Vietnam: A case study of the risk to aquaculture

In recent years, the Mekong delta has been strongly developed both for agriculture and aquaculture. However, there is scope for a negative impact of agriculture on aquaculture in term of production and quality of seafood products. Specifically, the large amount of pesticides imported and used in the Mekong delta not only help agriculture purposes but can also easily enter aquatic systems and affect aquaculture. Pesticides can be transported in the environment by chemo-dynamic procedures and hydrological processes. As a result, pesticides used in agriculture become dispersed and their residues in sediment, water and biota have been detected in the Mekong delta. This study investigated the overall pesticide process including pesticide use, modelling pesticide accumulation and evaluating the potential impact on aquaculture sites for some target aquatic species. The risk of pesticides use in the Mekong delta was addressed in three stages: (1) investigating current pesticide use status in the Mekong delta; (2) modelling pesticide loss and accumulation; (3) classifying pesticide risk areas for aquaculture of target cultured species. A survey of 334 farms covering a total area of ~20,000km2 in the Mekong delta took place between 2008 and 2009. Information on pesticide types and quantities was recorded using questionnaires, and it was found that 96 pesticides in 23 groups were popularly used for agricultural purposes. Dicarboximide, Carbamate and Conazole had the highest use at ~3000, ~2000 and ~2000 g/ha/year respectively. The survey revealed an increase in pesticide use per hectare since previous surveys in the Mekong delta in 1994, 2000, and 2004. However, the highly persistent compounds (WHO classification classes II, III and IV) appeared to have reduced in use. Insecticides previously represented >50% of the total pesticides used, however, the resent survey has shown their use has decreased to ~38%.There was a parallel increase in use of fungicides from previous levels of <30% of total pesticides to more recently ~41%. The combination of pesticide information and geo-location data enabled display and analysis of this data spatially using a Geographic Information System (GIS). A pesticide loss and accumulation model was established through combination of several sub-models including sediment loss and accumulation, direct loss, and water runoff, all of which were implemented and integrated within the GIS environment. MUSLE (Modified Universal Soil Loss Equation) was used to estimate sediment loss and accumulation in the Mekong delta and the Curve Number method (CN Method) was applied to predict water runoff and discharges and flow accumulation. Modelling commenced from the first pesticide application in April, based on 4 day time-steps. All mathematical calculations run within each time step automatically reiterated in the following time step with the new input datasets. The results from fuzzy classification of the pesticide model outcomes were considered in terms of the 96hr lethal concentration (LC50) in order to classify the risk and non-risk areas for catfish and tiger shrimp culture. The sediment loss and accumulation model shows that the highest loss of sediment was in the rainy season, especially in May to October. Vegetables and short term crop areas were found be most strongly eroded. The MUSLE model showed that the highest sediment accumulation was in the hilly areas (~1066.42 tonne/ha/year); lower in riverside areas (~230.39 tonne/ha/year) and lowest in flooded paddy areas (~150.15tonne/ha/year). Abamectin was used as an example throughout this study to estimate pesticide loss and its effects on aquaculture. The results showed that pesticide loss by runoff and sediment loss is less than the loss by half-life degradation (for Abamectin specifically). Accumulation of Abamectin occurred at highest rate in May and October and decreased with time. The spatial models showed that pesticide residues concentrated in the river and riverside areas. In order to evaluate the acute toxicity impacts, three levels of water depth in ponds were modelled as culture depths for catfish and tiger shrimp. The results show that the highest risk areas for catfish occurred in May and October with ~333,000 and ~420,000 ha at a pond depth of 0.5 m; ~136,000 and ~183,000 ha at a pond depth of 1.0 m; and ~10,840 and ~19,000 ha at a pond depth of 1.5 m. Risk areas for catfish mainly concentrated at the riverside and in part of the coastal areas. For tiger shrimp, the risk periods during the year were similar to those found for catfish. The highest risk areas for shrimp were ~648,000 and ~771,000 ha at 0.5 m pond depth; ~346,000 and ~446,700 ha at 1.0 m pond depth; and ~185,000 and ~250,000 ha at 1.5 m pond depth. Overall, deeper ponds reduced the risk. This study has developed a method to evaluate the negative impact of input pesticides to the environment from agricultural use related to fluctuation of aquaculture risk areas. The research indicates the potential relationship between pesticide input and the risk areas for aquaculture. The model has several significant uses: 1) it can provide information to policy makers for a more harmonized development of both aquaculture and agriculture in the Mekong delta in the future, 2) it provides data for aquaculture investment analysis to decrease the hazards caused by pesticide impacts, and 3) it provides a model capable of application to wide field scenarios and suitable for any pesticide type

Modeling the Effect of Land Use and Climate Change Scenarios on the Water Flux of the Upper Mara River Flow, Kenya

Increasingly erratic flow in the upper reaches of the Mara River, has directed attention to land use change as the major cause of this problem. The semi-distributed hydrological model SWAT and Landsat imagery were utilized in order to 1) map existing land use practices, 2) determine the impacts of land use change on water flux; and 3) determine the impacts of climate change scenarios on the water flux of the upper Mara River. This study found that land use change scenarios resulted in more erratic discharge while climate change scenarios had a more predictable impact on the discharge and water balance components. The model results showed the flow was more sensitive to the rainfall changes than land use changes but land use changes reduce dry season flows which is a major problem in the basin. Deforestation increased the peak flows which translated to increased sediment loading in the Mara River

Assessment of hydrology and dynamics of pesticides in a tropical headwater catchment in Northern Thailand

The dissertation deals with assessment of hydrology and the dynamics of pesticides in a tropical headwater catchment in northern Thailand. Rainfall and runoff characteristics are recorded and investigated, pesticide dynamics during single events are monitored and studied. Finally, a hydrological model is applied.Die Doktoarbeit beschäftigt sich mit der Hydrologie und der Dynamik von Pestiziden in einem tropischen Quelleinzugsgebiet in Nordthailand. Niederschlag und Abfluss werden gemessen und analysiert und die Dynamik von Pestiziden während einzelner Ereignisse wird beobachtet. Abschließend wird ein hydrologisches Model angewandt

Development of Real-Time Surface Water Abstraction Management Tools

Efficient use of available water resources to meet demand, whilst maintaining the quality of the aquatic environment has become increasingly important. Water quality challenges associated with diffuse agricultural pollutions have also become widely recognized problems globally. This thesis presents the development of new approaches to improve surface water abstraction management with a view to mitigate the challenges associated with increasing pressures on availability of water resources for public water supply and diffuse agricultural pollution. The first part of the thesis presents the development of a real-time surface water abstraction management scheme that integrates a conceptual rainfall-runoff model, a Bayesian inference based uncertainty analysis tool and a water resources management model that incorporates various operating rules to represent real-world operational constraints. The developed approach enables efficient utilization of available water resources and thus provides improved capability to deal with emerging issues of increasing demand, climate adaptation planning and associated policy reforms. The second part of the thesis describes the development of a new travel time based physically distributed metaldehyde prediction model, which enables water infrastructure operators to consider informed surface water abstraction decisions. Metaldehyde is a soluble synthetic aldehyde pesticide used globally in agriculture and has caused recent concerns due to high observed levels in surface waters utilized for potable water supply. The model provides new approach to represent spatially and temporally disaggregated runoff generation, routing and build-up/wash-off processes using a grid based structure in a GIS environment. Furthermore, a state-of-the-art Monte Carlo based spatial uncertainty analysis tool is employed to assess uncertainties in the metaldehyde prediction model. The structure of the metaldehyde model combined with the availability of high spatiotemporal resolution data has enabled the application of spatial uncertainty analysis of the catchment scale metaldehyde model, which is currently lacking in water quality modelling studies

Approche géomatique de la variabilité spatio-temporelle de la contamination microbienne des eaux récréatives

L’objectif général de cette thèse est de caractériser la dynamique des transferts des bactéries fécales à l’aide d’une modélisation spatio-temporelle, à l’échelle du bassin versant (BV) dans une région agricole et à l’échelle événementielle. Ce projet vise à mieux comprendre l'influence des processus hydrologiques, les facteurs environnementaux et temporels impliqués dans l’explication des épisodes de contamination microbienne des eaux récréatives. Premièrement, un modèle bayésien hiérarchique a été développé pour quantifier et cartographier les niveaux de probabilité des eaux à être contaminées par des effluents agricoles, sur la base des données spectrales et des variables géomorphologiques. Par cette méthode, nous avons pu calculer les relations pondérées entre les concentrations d’Escherichia coli et la distribution de l’ensemble des paramètres agro-pédo-climatiques qui régissent sa propagation. Les résultats ont montré que le modèle bayésien développé peut être utilisé en mode prédictif de la contamination microbienne des eaux récréatives. Ce modèle avec un taux de succès de 71 % a mis en évidence le rôle significatif joué par la pluie qui est la cause principale du transport des polluants. Deuxièmement, le modèle bayésien a fait l’objet d'une analyse de sensibilité liée aux paramètres spatiaux, en utilisant les indices de Sobol. Cette démarche a permis (i) la quantification des incertitudes sur les variables pédologiques, d’occupation du sol et de la distance et (2) la propagation de ces incertitudes dans le modèle probabiliste c'est-à-dire le calcul de l’erreur induite dans la sortie par les incertitudes des entrées spatiales. Enfin, une analyse de sensibilité des simulations aux différentes sources d’incertitude a été effectuée pour évaluer la contribution de chaque facteur sur l’incertitude globale en prenant en compte leurs interactions. Il apparaît que sur l’ensemble des scénarios, l’incertitude de la contamination microbienne dépend directement de la variabilité des sols argileux. Les indices de premier ordre de l’analyse de Sobol ont montré que parmi les facteurs les plus susceptibles d’influer la contamination microbienne, la superficie des zones agricoles est le premier facteur important dans l'évaluation du taux de coliformes. C’est donc sur ce paramètre que l’attention devra se porter dans le contexte de prévision d'une contamination microbienne. Ensuite, la deuxième variable la plus importante est la zone urbaine avec des parts de sensibilité d’environ 30 %. Par ailleurs, les estimations des indices totaux sont meilleures que celles des indices de premier ordre, ce qui signifie que l’impact des interactions paramétriques est nettement significatif pour la modélisation de la contamination microbienne Enfin, troisièmement, nous proposons de mettre en œuvre une modélisation de la variabilité temporelle de la contamination microbiologique du bassin versant du lac Massawippi, à partir du modèle AVSWAT. Il s'agit d'une modélisation couplant les composantes temporelles et spatiales qui caractérisent la dynamique des coliformes. La synthèse des principaux résultats démontrent que les concentrations de coliformes dans différents sous-bassins versants se révèlent influencées par l’intensité de pluie. La recherche a également permis de conclure que les meilleures performances en calage sont obtenues au niveau de l'optimisation multi-objective. Les résultats de ces travaux ouvrent des perspectives encourageantes sur le plan opérationnel en fournissant une compréhension globale de la dynamique de la contamination microbienne des eaux de surface.Abstract : The aim of this study was to predict water faecal contamination from a bayesian probabilistic model, on a watershed scale in a farming area and on a factual scale. This project aims to better understand the influence of hydrological, environmental and temporal factors involved in the explanation of microbial contamination episodes of recreational waters. First, a bayesian probabilistic model: Weight of Evidence was developed to identify and map the probability of water levels to be contaminated by agricultural effluents, on the basis of spectrals data and geomorphologic variables. By this method, we were able to calculate weighted relationships between concentrations of Escherichia coli and distribution of key agronomic, pedologic and climatic parameters that influence the spread of these microorganisms. The results showed that the Bayesian model that was developed can be used as a prediction of microbial contamination of recreational waters. This model, with a success rate of 71%, highlighted the significant role played by the rain, which is the main cause of pollution transport. Secondly, the Bayesian probabilistic model has been the subject of a sensitivity analysis related to spatial parameters, using Sobol indications. This allowed (1) quantification of uncertainties on soil variables, land use and distance and (2) the spread of these uncertainties in the probabilistic model that is to say, the calculation of induced error in the output by the uncertainties of spatial inputs. Lastly, simulation sensitivity analysis to the various sources of uncertainty was performed to assess the contribution of each factor on the overall uncertainty taking into account their interactions. It appears that of all the scenarios, the uncertainty of the microbial contamination is directly dependent on the variability of clay soils. Sobol prime indications analysis showed that among the most likely to influence the microbial factors, the area of farmland is the first important factor in assessing the coliforms. Importance must be given on this parameter in the context of preparation for microbial contamination. Then, the second most important variable is the urban area with sensitivity shares of approximately 30%. Furthermore, estimates of the total indications are better than those of the first order, which means that the impact of parametric interaction is clearly significant for the modeling of microbial contamination. Thirdly, we propose to implement a temporal variability model of microbiological contamination on the watershed of Lake Massawippi, based on the AVSWAT model. This is a model that couples the temporal and spatial components that characterize the dynamics of coliforms. The synthesis of the main results shows that concentrations of Escherichia coli in different sub-watersheds are influenced by rain intensity. Research also concluded that best performance is obtained by multi-objective optimization. The results of these studies show the prospective of operationally providing a comprehensive understanding of the dynamics of microbial contamination of surface water