The shedding of pathogens by infected humans enables the use of sewage monitoring to
conduct wastewater-based epidemiology (WBE). Although most WBE studies use data from
large sewage treatment plants, timely data from smaller catchments are needed for
targeted public health action. Traditional sampling methods, like autosamplers or grab
sampling, are not conducive to quick
ad hoc
deployments and
high-resolution monitoring at these smaller scales. This study develops and validates a
cheap and easily deployable passive sampler unit, made from readily available
consumables, with relevance to the COVID-19 pandemic but with broader use for WBE. We
provide the first evidence that passive samplers can be used to detect SARS-CoV-2 in
wastewater from populations with low prevalence of active COVID-19 infections (0.034 to
0.34 per 10,000), demonstrating their ability for early detection of infections at three
different scales (lot, suburb, and city). A side by side evaluation of passive samplers
(
n
= 245) and traditionally collected wastewater samples
(
n
= 183) verified that the passive samplers were sensitive at
detecting SARS-CoV-2 in wastewater. On all 33 days where we directly compared
traditional and passive sampling techniques, at least one passive sampler was positive
when the average SARS-CoV-2 concentration in the wastewater equaled or exceeded the
quantification limit of 1.8 gene copies per mL (
n
= 7). Moreover, on 13
occasions where wastewater SARS-CoV-2 concentrations were less than 1.8 gene copies per
mL, one or more passive samplers were positive. Finally, there was a statistically
significant (
p
< 0.001) positive relationship between the
concentrations of SARS-CoV-2 in wastewater and the levels found on the passive samplers,
indicating that with further evaluation, these devices could yield semi-quantitative
results in the future. Passive samplers have the potential for wide use in WBE with
attractive feasibility attributes of cost, ease of deployment at small-scale locations,
and continuous sampling of the wastewater. Further research will focus on the
optimization of laboratory methods including elution and extraction and continued
parallel deployment and evaluations in a variety of settings to inform optimal use in
wastewater surveillance.
Wastewater treatment plants (WWTP) typically have a service life of several decades. During this service life, external factors, such as changes in the effluent standards or the loading of the WWTP may change, requiring WWTP performance to be optimized. WWTP modelling is widely accepted as a means to assess and optimize WWTP performance. One of the challenges for WWTP modelling remains the prediction of water quality at the inlet of a WWTP. Recent applications of water quality sensors have resulted in long time series of WWTP influent quality, containing valuable information on the response of influent quality to e.g., storm events. This allows the development of empirical models to predict influent quality. This paper proposes a new approach for water quality modelling, which uses the measured hydraulic dynamics of the WWTP influent to derive the influent water quality. The model can also be based on simulated influent hydraulics as input. Possible applications of the model are filling gaps in time series used as input for WWTP models or to assess the impact of measures such as real time control (RTC) on the performance of wastewater systems.
This paper introduces the application of fibre-optic distributed temperature sensing (DTS) in combined sewer systems. The DTS-technique uses a fibre-optic cable that is inserted into a combined sewer system in combination with a laser instrument that performs measurements and logs the data. The DTS-technique allows monitoring in-sewer temperatures with dense spatial and temporal resolutions. The installation of a fibre-optic cable in a combined sewer system has proven feasible. The use of a single instrument in an easy accessible and safe location that can simultaneously monitor up to several hundreds of monitoring locations makes the DTS set-up easy in use and nearly free of maintenance. Temperature data from a one-week monitoring campaign in an 1,850 m combined sewer system shows the level of detail with which in-sewer processes that affect wastewater temperatures can be studied. Individual discharges from house-connections can be tracked in time and space. With a dedicated cable configuration the confluence of wastewater flows can be observed with a potential to derive the relative contributions of contributary flows to a total flow. Also, the inflow and in-sewer propagation of stormwater can be monitored.
A major drawback of separate sewer systems is the occurrence of illicit connections: unintended sewer cross-connections that connect foul water outlets from residential or industrial premises to the storm water system and/or storm water outlets to the foul sewer system. The amount of unwanted storm water in foul sewer systems can be significant resulting in a number of detrimental effects on the performance of the wastewater system. Efficient removal of storm water inflows into foul sewers requires knowledge on the exact locations of the inflows. This paper presents a monitoring technique that can be used to localize illicit storm water inflows into foul sewer systems: Distributed Temperature Sensing (DTS).Data results from two monitoring campaigns in foul sewer systems in the Netherlands and Germany show the level of detail with which in-sewer processes can be studied. Storm water inflow can be detected as long as the temperature of this inflow differs from the in-sewer temperatures prior to the event. Also, the insewer propagation of storm water can be monitored, enabling a detailed view on advection-dispersion and mixing processes.
Long-term and high-frequency in-sewer monitoring opens up a broad range of possibilities to study (influences on) water quantity and quality variations. Using data from the Eindhoven wastewater system in The Netherlands both dry weather flow and wet weather flow situations have been studied. For approximately 160 dry weather days mean diurnal variations of flow and pollutant concentrations have been derived. For wet weather situations (≈ 40 storm events) peak load factors have been studied. Generally, peak load factors for all considered pollutant parameters are larger than one. Peak load factors for particulate matter are larger than for dissolved constituents. Also, the smallest catchment area consistently shows the largest mean peak factors and vice versa.
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