Sewage networks for the transport of wastewater from industrial, commercial, and residential areas to sewage treatment plants are essential elements of the water infrastructure. As the construction and maintenance of wastewater networks are cost-intensive, a large number of research projects are dedicated to the optimization of such infrastructures [1
In recent years, the focus has been on the development of optimization algorithms for layout and component size of sewer networks [1
]. Wastewater discharges as a main boundary condition for optimization are often regarded as “given” [1
] without going into details in terms of data sources. In other works, the amount of wastewater is merely estimated, e.g., based on the drinking-water consumption of larger supply areas and provided with a peak factor [2
]. Here, wrong assumptions about the amount of wastewater may lead to an oversized or undersized system [5
In the work of Willuweit and O’Sullivan [6
] a combination of models is used to simulate water demand, water supply, wastewater, and runoff in urban areas, under changing land-use and climate scenarios. In the water balance approach, it is assumed that the amount of wastewater produced corresponds to the domestic water consumption. To estimate the water demand, flows monitored in district metered areas are evenly distributed to cells of 4 ha size, using GIS software.
To determine the amount of wastewater inflow into existing drainage systems, the worksheet 118 [7
] of the German Association for Water, Wastewater and Waste (DWA) recommends measurements of dry weather runoff with long measurement periods, if possible, in different seasons, as well as additional measurements in commercial, industrial, or tourist areas. However, the collection of data with a temporally and spatially high-resolution involves a great deal of effort. In addition, data protection and company secrecy restrict the availability of such datasets. Hence, procedures are needed to determine the volume of wastewater with high spatial resolution, based on freely accessible data.
A common approach to determine the domestic water use is by population figures. For instance, Schiller and Bräuer [8
] present a method using population figures from official statistics, disaggregated by building footprints from ATKIS (official German topographic–cartographic dataset). The main part of their method is the classification of three settlement/municipality types and ten building types based on topographic geodata and building footprints from the ATKIS basic digital elevation model (DEM) with GIS tools. Building footprints and information about the use of buildings can also be derived from OpenStreetMap (OSM) datasets. Bakillah et al. [9
] use points of interest (POI) from OSM as indicators for high or low population densities, to disaggregate population figures of the city of Hamburg into a grid (500 m² per grid cell). They then distribute the population of each grid cell proportionally to the OSM building footprint size within it. Kunze [10
] uses building attributes stored in OSM data to describe the share of non-residential use in existing buildings. Fan et al. [11
] derive different building types from semantic information and the shape of the building floor area in OSM.
OSM geodata are maintained and continuously expanded by the OSM community. This way of generating and collecting data is called volunteered geographical information (VGI) [12
]. Since the data are collected by nonprofessionals, the preparation and quality assurance of VGI data, in general, and of OSM data, in particular, are much more demanding than of data collected specifically for a particular research question. A large number of articles is devoted to quality assessment and assurance at VGI in general [13
]. Particularly for OSM building datasets, the data quality is the subject of various studies regarding completeness [20
], building density [22
], and accuracy of geometries [23
]. Heterogeneity and incompleteness of the data is a common challenge. A higher data density and accuracy of OSM buildings in cities compared to rural regions can be explained by the number of local participants in the OSM project, which is generally larger in cities [15
]. The same findings are noted for land-use data [16
]. In addition, incorrect data collection by nonprofessionals is a common difficulty, as is the lack of metadata or the ability to verify data [17
Nevertheless, VGI has proved to be a useful data source in many fields of application and scientific questions, since—as the word “volunteered” suggests—they are voluntarily made available to the public and, thus, the user does not have to spend any time on data collection. OSM data are freely available and may be used under the Open Database License [25
]. Application examples for VGI are the mapping of flood hazards [26
], disaster management [27
], modeling of energy infrastructure [25
], validation of land-use maps [29
], or ecological monitoring [30
]. The application of OSM data in urban-planning contexts has so far been investigated primarily in the field of transport infrastructure planning [31
] and the analysis of settlement structures [8
]. In the field of urban water management, the OSM road network was used to generate virtual wastewater networks [34
]. The connecting element between settlement structure and sewage network was not considered in the mentioned work: wastewater from residential buildings, industry and commerce, or other uses that is transported to a treatment plant along the sewage network.
In this article, a method for the estimation of the wastewater volumes based on OSM data is presented. The parameters used for the calculations are optimized by comparing the estimated wastewater volumes in the catchment area of wastewater treatment plants (WWTPs) and sewage pumping stations (SPSs) with measured dry weather inflows. As a sample application, estimated wastewater discharges are spatially intersected with land-use plans, to develop a scenario-capable tool for the integral planning of settlement structural measures.
The method introduced here represents an instrument to estimate discharges of wastewater from domestic, commercial, and industrial use on a high spatial resolution. The approach adds a new application for VGI to the existing methods. Using OSM as the main data source makes the method transferable to other regions. Where quality issues of OSM building data generate uncertainty, especially in rural regions, additional data from different sources, e.g., authoritative data, may be used to identify missing buildings. To achieve higher accuracy in the classification process, OSM buildings data could be enhanced with POI from OSM, or an analysis of shape and size of building footprints could be applied.
For the calibration of the discharge rates qR, qI, and qC, finding suitable target values is an essential step. Extraneous water, such as groundwater infiltration through leakages and rainwater or snowmelt infiltration into the sewage system, leads to temporarily high discharge rates, which increase the difficulty of finding a target value for the inflow. As this is a crucial step of the method, different approaches for the determination should be tested in future research, e.g., hydrograph separation or calibration with consumption rates of drinking water. Where long-term series of inflow to WWTPs and SPSs are available, the variability of wastewater discharges can be estimated by minimum and maximum values for different spaces of time. Seasonally varying target values derived in this way could improve the accuracy of the results, especially for small catchment areas of WWTPs. The method shown here, which uses one target value per WWTP or SPS, can be applied when inflow data sources are limited.
Wastewater discharges aggregated along the sewer networks in the study area generate realistic inflows to WWTPs and SPSs, which differ by a maximum of 31% from the target inflow when this value exceeds 50 m³/d. In small catchments, individual behavior of water consumption may lead to overestimates or underestimations. Here, the integration of peak factors to represent variability of wastewater discharges could improve the estimation. The estimated values cannot replace time-consuming measurements. However, the method can fill the gap of input data for model-building and urban water management, especially for research purposes, when measured data do not exist or their availability is legally restricted. By intersection of discharges with land-use plans, wastewater yield factors are derived as a scenario-capable tool, which enables us to simulate the effect of land-use change in modeling future scenarios.