To which extent illicit drugs are transformed during in-sewer transport, depends on a number of factors: i) substance-specific transformation rates, ii) environmental conditions, iii) point of discharge (location of drug user) and iv) sewer network properties, primarily hydraulic residence time (HRT) and the ratio of biofilm contact area to wastewater volume (A/V). Assessing associated uncertainties typically requires numerous simulations. Therefore, we propose a new two-step modeling framework: 1) Quantify hydrodynamic conditions. This computationally demanding step was performed once in SWMM to derive HRT and A/V for each potential point of discharge (node) in three catchments of different size. 2) Estimate biomarker loss. In this step, Monte Carlo simulations were performed for defined scenarios. Depending on assumptions about drug user distribution and prevalence, a number of nodes was sampled. For each node an empirical first-order transformation model was applied with flow-path-corresponding HRT and A/V from step 1. Biotic and abiotic transformation rates were sampled from distributions combining variability of different biofilms. In our modeling study, median losses were >30% for amphetamine, 6-monoacetylmorphine and 6-acetylcodeine in all three catchments with high uncertainty (5%-100% loss), which would imply a systematic underestimation of consumption when neglecting in-sewer processes. Median losses for 21 other investigated biomarkers were <10% with different uncertainty ranges - "no substantial transformation" was confirmed for nine substances in a real sewer segment with a 2-h residence time. Transferability of these results must be tested for other catchments. To further reduce uncertainty, mainly additional knowledge on transformation rates, particularly in biofilm, and their distribution across a sewer network is needed to update model input objectively. Our approach allows efficient testing and, furthermore, can be expanded for many other human biomarkers. Accounting for biomarker stability during in-sewer transport will avoid biased estimates and further improve wastewater-based epidemiology.
Collecting precise real-time information on urban drainage system performance is essential to identify, predict, and manage critical loading situations, such as urban flash floods and sewer overflows. Although emerging low-power wireless communication techniques allow efficient data transfers with great above-ground performance, for underground or indoor applications in a large coverage range are difficult to achieve due to physical and topological limitations, particularly in dense urban areas. In this paper, we first discuss the range limitations of the LoRaWAN standard based on a systematic evaluation of a long-term operation of a sensor network monitoring in-sewer process dynamics. Analyses reveal an-on average-five-fold higher data packet loss for sub-surface nodes, which steadily grows with increasing distance to the gateway. Second, we present a novel LPWAN concept based on the LoRa R technology that enhances transmission reliability, efficiency, and flexibility in range-critical situations through meshed multi-hop routing and ensures a precise time-synchronization through optional GPS or DCF77 long-wave time signaling. Third, we illustrate the usefulness of the newly developed concept by evaluating the radio transmission performance for two independent full-scale field tests. Test results show that the synchronous LoRa mesh network approach clearly outperforms the standard LoRaWAN technique with regard to the reliability of packet delivery when transmitting from range-critical locations. Hence, the approach is expected to generally ease data collection from difficult-to-access locations such as underground areas. INDEX TERMS Environmental engineering, Internet of Things, LoRaWAN, mesh networks, time-division multiple access, water pollution, wide area networks, wireless sensor networks, urban drainage.
Abstract. The performance of urban drainage systems is typically examined using hydrological and hydrodynamic models where rainfall input is uniformly distributed, i.e., derived from a single or very few rain gauges. When models are fed with a single uniformly distributed rainfall realization, the response of the urban drainage system to the rainfall variability remains unexplored. The goal of this study was to understand how climate variability and spatial rainfall variability, jointly or individually considered, affect the response of a calibrated hydrodynamic urban drainage model. A stochastic spatially distributed rainfall generator (STREAP – Space-Time Realizations of Areal Precipitation) was used to simulate many realizations of rainfall for a 30-year period, accounting for both climate variability and spatial rainfall variability. The generated rainfall ensemble was used as input into a calibrated hydrodynamic model (EPA SWMM – the US EPA's Storm Water Management Model) to simulate surface runoff and channel flow in a small urban catchment in the city of Lucerne, Switzerland. The variability of peak flows in response to rainfall of different return periods was evaluated at three different locations in the urban drainage network and partitioned among its sources. The main contribution to the total flow variability was found to originate from the natural climate variability (on average over 74 %). In addition, the relative contribution of the spatial rainfall variability to the total flow variability was found to increase with longer return periods. This suggests that while the use of spatially distributed rainfall data can supply valuable information for sewer network design (typically based on rainfall with return periods from 5 to 15 years), there is a more pronounced relevance when conducting flood risk assessments for larger return periods. The results show the importance of using multiple distributed rainfall realizations in urban hydrology studies to capture the total flow variability in the response of the urban drainage systems to heavy rainfall events.
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