Substrate clogging is by far the biggest operational problem of vertical flow constructed wetlands. The term "substrate clogging" summarises several processes which lead to reduction of the infiltration capacity at the substrate surface. The lower infiltration rate causes a reduced oxygen supply and further leads to a rapid failure of the treatment performance. Reasons for substrate clogging include accumulation of suspended solids, surplus sludge production, chemical precipitation and deposition in the pores, growth of plant-rhizomes and roots, generation of gas and compaction of the clogging layer. However, it is not clear how much each process contributes to the clogging process. Detailed investigations were carried out at pilot-scale constructed wetlands (PSCWs) using a variety of methods: e.g. soil physical investigations, microbial methods, and various analysis methods of drinking water and wastewater. The paper shows the results of these investigations and presents an equation to calculate the theoretical clogging time.
Different approaches for quantification of pollution loads discharged from combined sewer networks into surface water bodies have been observed over the last few years and decades, but a large number of unresolved problems still remain. Many monitoring campaigns have been based on manual or automated spot sampling - with the long known limitations of this method such as sampling errors and errors due to sample conservation, transport and preparation. On the other hand, only recently have sensors became available which are suitable for continuous application in sewer networks. A large number of practical problems still have to be solved before continuous monitoring in sewer networks will be successful. Additionally, most of the applicable sensors are based on surrogate methods which results in a considerable effort for reference measurements for sensor calibration. Finally, it has to be considered that, depending on the sewer network topography, deposition and remobilisation of pollutants varies considerably, which limits the generality of monitoring results and, subsequently, their applicability as a base for the design of storm water tanks or combined sewer overflows (CSO). A monitoring station for continuous monitoring of load discharges from a CSO has been installed and operated for more than one year. The design and equipment of the measurement station, operational experiences and results are given in this paper.
Within the last years a trend towards in-situ monitoring can be observed, i.e. most new sensors for water quality monitoring are designed for direct installation in the medium, compact in size and use measurement principles which minimise maintenance demand. Ion-sensitive sensors (Ion-Sensitive-Electrode--ISE) are based on a well known measurement principle and recently some manufacturers have released probe types which are specially adapted for application in water quality monitoring. The function principle of ISE-sensors, their advantages, limitations and the different methods for sensor calibration are described. Experiences with ISE-sensors from applications in sewer networks, at different sampling points within wastewater treatment plants and for surface water monitoring are reported. An estimation of investment and operation costs in comparison to other sensor types is given.
Abstract. Environmental modeling studies aim to infer the impacts on environmental variables that are caused by natural and human-induced changes in environmental systems. Changes in environmental systems are typically implemented as discrete scenarios in environmental models to simulate environmental variables under changing conditions. The scenario development of a model input usually involves several data sources and perhaps other models, which are potential sources of uncertainty. The setup and the parametrization of the implemented environmental model are additional sources of uncertainty for the simulation of environmental variables. Yet to draw well-informed conclusions from the model simulations it is essential to identify the dominant sources of uncertainty. In impact studies in two Austrian catchments the eco-hydrological model Soil and Water Assessment Tool (SWAT) was applied to simulate discharge and nitrate-nitrogen (NO3--N) loads under future changing conditions. For both catchments the SWAT model was set up with different spatial aggregations. Non-unique model parameter sets were identified that adequately reproduced observations of discharge and NO3--N loads. We developed scenarios of future changes for land use, point source emissions, and climate and implemented the scenario realizations in the different SWAT model setups with different model parametrizations, which resulted in 7000 combinations of scenarios and model setups for both catchments. With all model combinations we simulated daily discharge and NO3--N loads at the catchment outlets. The analysis of the 7000 generated model combinations of both case studies had two main goals: (i) to identify the dominant controls on the simulation of discharge and NO3--N loads in the two case studies and (ii) to assess how the considered inputs control the simulation of discharge and NO3--N loads. To assess the impact of the input scenarios, the model setup, and the parametrization on the simulation of discharge and NO3--N loads, we employed methods of global sensitivity analysis (GSA). The uncertainties in the simulation of discharge and NO3--N loads that resulted from the 7000 SWAT model combinations were evaluated visually. We present approaches for the visualization of the simulation uncertainties that support the diagnosis of how the analyzed inputs affected the simulation of discharge and NO3--N loads. Based on the GSA we identified climate change and the model parametrization as being the most influential model inputs for the simulation of discharge and NO3--N loads in both case studies. In contrast, the impact of the model setup on the simulation of discharge and NO3--N loads was low, and the changes in land use and point source emissions were found to have the lowest impact on the simulated discharge and NO3--N loads. The visual analysis of the uncertainty bands illustrated that the deviations in precipitation of the different climate scenarios to historic records dominated the changes in simulation outputs, while the differences in air temperature showed no considerable impact.
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