Microplastic river emissions are known to be one of the major sources for marine microplastic pollution. Especially urbanized estuaries localized at the land-sea interface and subjected to microplastic emissions from various sources exhibit a high microplastic discharge potential to adjacent coasts. To adapt effective measures against microplastic emissions a more detailed knowledge on the importance of various microplastic sources is necessary. As field data is scarce we combined different approaches to assess microplastic emissions into the Warnow estuary, southwestern Baltic Sea. Resulting microplastic emission estimates are based on in-situ measurements for the catchment emissions, whereas for the remaining microplastic sources within the estuary literature data on microplastic abundances, and various parameters were used (e.g. demographical, hydrological, geographical). The evaluation of the different emission scenarios revealed that the majority of microplastic is likely discharged by the Warnow river catchment (49.4%) and the separated city stormwater system (43.1%) into the estuary, followed by combined sewer discharges (6.1%). Wastewater treatment plant emissions exhibit the lowest percentage (1.4%). Our approach to estimate anti-fouling paint particles emissions from leisure and commercial shipping activities was associated with highest uncertainties. However, our results indicate the importance of this source highlighting the necessity for future research on the topic. Based on our assumptions for microplastic retention within the estuary, we estimate a potential annual emission of 152–291 billion microplastics (majority within the size class 10–100 µm) to the Baltic Sea. Considering all uncertainties of the different applied approaches, we could assess the importance of various microplastic sources which can be used by authorities to prioritize and establish emission reduction measures. Additionally, the study provides parameters for microplastic emission estimates that can be transferred from our model system to other urbanized Baltic estuaries.
For the optimization of sewer networks and integration of water management in urban planning, estimations of wastewater discharges at a high spatial resolution are a key boundary condition. In many cases, these data are not available or, for reasons of data protection and company secrecy, the data are not accessible for research purposes. Therefore, procedures are needed to determine the volume of wastewater with high spatial resolution, based on freely accessible data. The approach presented here uses mainly OpenStreetMap (OSM) data, combined with a dataset of the German official topographic–cartographic Information System (ATKIS), to estimate the volume of wastewater on a building level. By comparison with daily values of the dry weather inflow at pumping stations and sewage treatment plants, it is shown that the method can generate realistic results, if target inflows exceed 50 m³/d. Difficulties due to the effect of commuting and the individual use of the buildings have to be considered, as well as data-quality issues in the OSM dataset. As an application example, the generated wastewater discharges are spatially joined with land-use plans. The resulting wastewater yield factors serve as input data for decision-support tools in urban water planning or modeling tasks.
SWMM is an open-source model and software developed by the US EPA for the simulation of rainfall-runoff and routing in water bodies, sewer systems and wastewater infrastructures. It has been applied in numerous practical works and research projects. For a new SWMM model, objects such as nodes, links and catchments can either be drawn via SWMM’s graphical user interface (GUI) or specified manually in a plain text file in “.inp” format (“input file”). Since the required data regarding sewer geometries and river systems are usually available as spatial data in a GIS environment, there is a need for user-friendly interfaces for the model setup. SWMM contains neither an import function for geodata nor processing tools as provided in geographic information systems (GIS) such as the open-source software QGIS. Existing approaches were script-based or required commercial all-in-one products. We developed a free and open-source QGIS plugin to generate SWMM models from geodata and to import existing SWMM input files into QGIS. An application example is presented to demonstrate the basic features and usage of the plugin.
Land use changes can significantly influence the water balance, and thus especially the development of flood-triggering runoff peaks. Hence, it is advisable to assess possible changes already at the level of municipal planning. Since many different actors are usually involved in spatial planning, it is useful to provide a shared platform where stakeholders can access the same information to analyze and evaluate flood hazards. Therefore, a GIS routine for the prediction of soil sealing induced runoff peaks and resulting potential flooding in the watercourse was developed, which is embedded in a GIS based decision support system (GIS-DSS). The so-called storm water routine (SWR) is founded on preprocessed flood characteristics, calculated by means of hydrological/hydraulic models (described in part 1). The potential impact of land use change is assessed purely in GIS as flow difference which is routed through the river system. To validate this simplified method, a process model was set up with an exemplary land use change and its results were compared with the GIS-based results. For 16 of the 18 rainfall scenarios tested, the SWR provided very good to good agreement with the detailed model. For short and highly dynamic rain events the SWR approach is less reliable. Several supplements like the integration of LID are conceivable.
Achievements of good chemical and ecological status of groundwater (GW) and surface water (SW) bodies are currently challenged mainly due to poor identification and quantification of pollution sources. A high spatio-temporal hydrological and water quality monitoring of SW and GW bodies is the basis for a reliable assessment of water quality in a catchment. However, high spatio-temporal hydrological and water quality monitoring is expensive, laborious, and hard to accomplish. This study uses spatio-temporally low resolved monitored water quality and river discharge data in combination with integrated hydrological modelling to estimate the governing pollution pathways and identify potential transformation processes. A key task at the regarded lowland river Augraben is (i) to understand the SW and GW interactions by estimating representative GW zones (GWZ) based on simulated GW flow directions and GW quality monitoring stations, (ii) to quantify GW flows to the Augraben River and its tributaries, and (iii) to simulate SW discharges at ungauged locations. Based on simulated GW flows and SW discharges, NO3-N, NO2-N, NH4-N, and P loads are calculated from each defined SW tributary outlet (SWTO) and respective GWZ by using low-frequency monitored SW and GW quality data. The magnitudes of NO3-N transformations and plant uptake rates are accessed by estimating a NO3-N balance at the catchment outlet. Based on sensitivity analysis results, Manning’s roughness, saturated hydraulic conductivity, and boundary conditions are mainly used for calibration. The water balance results show that 60–65% of total precipitation is lost via evapotranspiration (ET). A total of 85–95% of SW discharge in Augraben River and its tributaries is fed by GW via base flow. SW NO3-N loads are mainly dependent on GW flows and GW quality. Estimated SW NO3-N loads at SWTO_Ivenack and SWTO_Lindenberg show that these tributaries are heavily polluted and contribute mainly to the total SW NO3-N loads at Augraben River catchment outlet (SWO_Gehmkow). SWTO_Hasseldorf contributes least to the total SW NO3-N loads. SW quality of Augraben River catchment lies, on average, in the category of heavily polluted river with a maximum NO3-N load of 650 kg/d in 2017. Estimated GW loads in GWZ_Ivenack have contributed approximately 96% of the total GW loads and require maximum water quality improvement efforts to reduce high NO3-N levels. By focusing on the impacts of NO3-N reduction measures and best agricultural practices, further studies can enhance the better agricultural and water quality management in the study area.
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