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18 results for “Sewer”
Water quality data from s::can sensors in the Barcelona sewer system
<p>Within the frame of the SCOREwater project, BCASA and s::can installed water quality sensors in the sewer system in three neighbourhoods with different socio-economic characteristics (Poblenou, Sant Gervasi and Carmel) in the City Of Barcelona. To prevent vandalism, the exact location of the sensors cannot be disclosed. The sensors are monitoring physico-chemical parameters in the sewer network. These data can be used for multiple purposes, both related to predictive maintenance of the sewer network and life style analysis of inhabitants of Barcelona:</p> <ul> <li>a better operation and maintenance of the sewer network</li> <li>minimizing odor episodes and corrosion from H2S</li> <li>try to prevent blocking from sediments</li> <li>detect spills into the sewer system from a construction site or illegal discharge</li> <li>learn population habits from analyzing the waste water from the three different neighborhoods</li> <li>to learn about how the inhabitants use pharmaceuticals or other compounds (perhaps abusing of it).</li> </ul>
Data and code from: "Building multidimensional tolerance landscapes to predict the population dynamics of bacteria exposed to antibiotics in urban sewers"
<p>City sewers harbor diverse bacterial communities exposed to various antibiotic residues resulting from human consumption and excretion. Although these residues typically occur at sub-inhibitory concentrations, they can still impact the growth rate and yield of susceptible wastewater bacteria. Many bacteria exhibit antibiotic tolerance through transient phenotypic changes. Antibiotic residues, combined with complex environmental factors like temperature and salinity, especially in coastal cities, contribute to non-additive interactions that modulate antibiotic tolerance and affect population dynamics.</p> <p>To better understand these interactions, we developed continuous multivariate tolerance landscapes for three bacterial species: <strong><em><span>Escherichia coli</span></em></strong>, the emerging pathogen <strong><em><span>Streptococcus suis</span></em></strong>, and the sewer-inhabiting <strong><em><span>Arcobacter cryaerophilus</span></em></strong>. We modeled their intrinsic growth rates and carrying capacities across complex environments, incorporating temperature, salinity, and concentrations of two antibiotics (ciprofloxacin and azithromycin).<span> Using</span> these multivariate tolerance curves, we predicted microbial population dynamics in two sewers of Barcelona, highlighting the importance of environmental complexity in shaping microbial responses to antibiotic stressors.</p> <p> </p> <p><strong>Usage</strong></p> <p>Users can perform the analysis by running the R script (TC3D.R) after the installation of all</p> <p>package mentioned in the preamble,<span> </span></p> <p>This folder contains:</p> <p>- 3 datasets with OD measures for the 3 species:</p> <p><span> </span>* data_acrya.xlsx</p> <p><span> </span>* data_ecoli.xlsx</p> <p><span> </span>* data_ssuis.xlsx</p> <p>- 1 excel files with metadata (plate, well, species, environmental conditions)</p> <p><span> </span>* map_plate_all.xlsx</p> <p>- 4 datasets giving time series of the flow and several measures including <span> </span>conductivity and <span> </span>temperaturefor 2 sewers of Barcelona obtained from sample cabines <span> </span>set during the implementation of SCOREWATER (ID:820751)</p> <p><span> </span>* carmel_flow.csv</p> <p><span> </span>* carmel_quality.csv</p> <p><span> </span>* poblenou_flow.csv</p> <p><span> </span>* poblenou_quality.csv</p> <p>- 1 C++ script compiled and run with the R TMB package:</p> <p><span> </span>* fit_growth_r_K_SS_treatment.cpp : computes the negative loglikelihood for r and K, and state DOs, given the observed DO, for the populations under one same environmental treatment (salinity * temperature * antibiotic), and computes the density-dependence parameter alpha from r and K using the Delta Method.</p> <p><br><br></p>
Supplementary data for "Ecological assessment of combined sewer overflow management practices through the analysis of benthic and hyporheic sediment bacterial assemblages of an intermittent stream"
<p><strong>Supplementary data for the Pozzi <em>et al.</em> paper entitled "Ecological assessment of combined sewer overflow management practices through the analysis of benthic and hyporheic microbial assemblages and a tracking of exogenous bacterial taxa in a peri-urban intermittent stream".</strong></p> <p># Created by Dr Adrien C. MEYNIER POZZI on June, 29th, 2023<br> # Part of DOmic research project funded by the Agence de l’Eau - Rhône Méditerranée Corse [AE-RMC, Project 2020 0702 DOmic, 2020-2023], and of the DOmic extension funded by the EUR H2O'Lyon [ANR-17-EURE-0018] of Université de Lyon<br> # Part of the Chaudanne river long-term experiment site belonging to the Observatoire de Terrain en Hydrologie Urbaine (OTHU)<br> # Part of the work conducted in the team on Opportinistic Bacterial Pathogen in the Environment (BPOE) led by Dr. Benoit Cournoyer<br> # Samples were obtained in 2 campaigns, corresponding to periods before (2010-2011) or after (2018) the implementation of the 91/271/EEC European Directive that limited Combined-Sewer Overflow (CSO) discharges to the Chaudanne river<br> # Samples consisted in surface water, benthic and hyporheic sediments taken in run, riffle and pool geomorphologic features, either upstream or downstream the CSO outlet, plus positive and negative controls</p> <table> <tbody> <tr> <td><strong>Metadata. Name and description of data tables provided as supplementary information</strong></td> </tr> <tr> <td><strong>Data Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Data S1. River hydrology variables and hydraulic gradients at surveyed transects</td> <td>Array to describe the hydrologic variables and gradients at the studied transects. Top line is header, second line is metadata for each recorded variable, and third line is the unit of the variable, if any.</td> </tr> <tr> <td>Data S2. Environmental variables (water physical-chemistry, nutrients, FIBs, MTEs, PAHs) with metadata</td> <td>An array to list environmental variables for all true samples (n=90) included in the study. Sample identifiers and dates are provided. First 8 rows list the CAS number, SANDRE number, unit, method, limit of quantification and norm for each variable, if any.</td> </tr> <tr> <td>Data S3. Hydrological indices and synthetic variables computed with ClustOfVar</td> <td>Hydrological indices computed for the river flow, precipitations and CSO overflows computed over a 3-week period preceding each sampling date.</td> </tr> <tr> <td>Data S4. Discharge events selected to compute CSO dilution ratios</td> <td>An array to describe CSO events included for the computation of the CSO dilution ratio (SI Data 6A) together with 6 tables and 3 figures (SI Data 6B to 6J) describing the CSO event ratio all year round over the studied period, as well as for events that occurred before or after the CSO was modified and during low flow or high flow season. In SI Data 6A, top line is header and second line is metadata for each recorded variable.</td> </tr> <tr> <td>Data S5. Raw environmental matrix for use in R</td> <td>An array to list experimental design and environmental variables for all true samples and controls. Several environmental variables were synthetized using the ClustOfVar method (Chavent et al (2012) 10.18637/jss.v050.i13). Format is directly usable in R software.</td> </tr> </tbody> </table> <p> </p>
Antwerp precipitation, open water streams and sewer system sensor data
<p>This csv dataset includes historical data for the period 2018-2020 from multiple sensors deployed in Antwerp that can help city services to have a clear view on the actual precipitation in different regions, the water level of different water flows as well as the water flows in the sewer system of the city. This data was used in CUTLER (visualized in Antwerp’s dashboard) to assist in the impact modelling of garden streets.</p> <p>The data set contains:</p> <p>- 6 water level sensors: lora.0004A30B00202D0C, lora.0004A30B00204B8B, lora.0004A30B00200BFE, lora.0004A30B0021F1D4, lora.0004A30B002041F6, lora.0004A30B001FC6DF</p> <p>- 4 pluvio meters: lora.0004A30B002025F5, lora.0004A30B00201DCC, lora.0004A30B001FF6F7, lora.0004A30B001FA140<br> <br> - 3 sewer level meters: lora.0004A30B001FD07B, lora.0004A30B0020112D, lora.0004A30B001F9B4B</p>
Characteristics of the urban sewer system and rat presence in Seattle
<p>Rats are abundant and ubiquitous in urban environments. There has been increasing attention to the need for evidence-based, integrated rat management and surveillance approaches because rats can compromise public health and impose economic costs. Yet there are few studies that characterize rat distributions in sewers and there are no studies that incorporate the complexity of sewer networks that encompass multiple sewer lines, all comprised of their own unique characteristics. To address this knowledge gap, this study identifies sewer characteristics that are associated with rat presence in the city of Seattle's urban sewer system. We obtained sewer baiting data from 1752 geotagged manholes to monitor rat presence and constructed generalized additive models to account for spatial autocorrelation. Sewer rats were unevenly distributed across sampled manholes with clusters of higher rat presence at upper elevations, within sanitary pipes, narrower pipes, pipes at a shallower depth, and older pipes. These findings are important because identifying features of urban sewers that promote rat presence may allow municipalities to target areas for rat control activities and sewer maintenance. These findings suggest the need to evaluate additional characteristics of the surface environment and identify the factors driving rat movement within sewers, across the surface, and between the surface and the sewers. </p>
Characteristics of the urban sewer system and rat presence in Seattle
Open the record for dataset details and reuse information.
Stepney Green Sewer Pipe
An old ventilation pipe ("stink pipe") from a sewer at thr north end of Stepney Greem, London. 110 photos taken in April 2021 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Data for "Fractal analysis of urban catchments and their representation in semi-distributed models: imperviousness and sewer system"
<p>The data set corresponds the data used in the paper : “Fractal analysis of urban catchments and their representation in semi-distributed models: imperviousness and sewer system”, published in 2017 in the Journal “Hydrology and Earth System Sciences” (http://www.hydrol-earth-syst-sci.net/).</p> <p>More precisely it corresponds to the matrices that are used in the fractal and multi-fractal analysis of the ten urban areas investigated in the paper.</p> <p> </p> <p>For each catchment, it is organised as follow:</p> <p>- catchment_name_conduit.asc : the matrix describing the sewer system.</p> <p>- catchment_name_OSM.asc : the matrix describing the impervious areas (roads and buildings) obtained via Open Street Map (www.openstreetmap.org)</p> <p>- catchment_name_OSM_house_only.asc : the matrix describing the “building” areas obtained via Open Street Map (www.openstreetmap.org)</p> <p>- catchment_name_imperviousness.asc : the matrix describing the representation of imperviousness in operational semi-distributed models.</p> <p> </p> <p>More details can be found in the paper.</p>
Training images for sewer inlet detector
<p>This package consists of a zipped directory with positive and negative image samples of sewer inlets. The samples can be used to train computer vision classification algorithms.</p>
Sewer Vent Chimney
A large red brick sewer vent chimney next to the Regents Canal south of Rhodeswell Road, Mile End, London. 390 photos taken in September 2021 with a Sony a7R III and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Time series of flow measurement and water quality monitoring at a large combined sewer overflow in Berlin
<p>The table contains 3 years CSO monitoring time series, already split in 22 separated CSO events. All details about the monitoring set up in the papers and reports indicated below.</p> <p>Fields:</p> <ul> <li>evtID: event ID</li> <li>myDateTime; date/time</li> <li>v: m/s velocity</li> <li>Q: m³/s flow</li> <li>TSS: mg/l TSS concentration</li> <li>COD: mg/l COD concentration</li> <li>CODf: mg/l dissolved COD concentration</li> <li>EC: µS/cm electric conductivity</li> <li>NH4_N: mg/l NH4_N concentration</li> </ul> <p>TSS and COD have been measured with a spectrometer using a linear local calibration as presented in Lepot et al., 2016.</p> <p>ore information about the monitoring set up in </p> <p>Sandoval, S., Torres, A., Pawlowsky-Reusing, E., Riechel, M., Caradot, N. (2013): The evaluation of rainfall influence on CSO characteristics: the Berlin case study. Water Science & Technology Vol. 68 (12): 2683-2690 10.2166/wst.2013.524</p> <p>Caradot, N. (2012): Continuous Monitoring of Combined Sewer Overflows in the Sewer and the Receiving River: Return on Experience. Kompetenzzentrum Wasser Berlin gGmbH; MIA-CSO Project Report</p> <p>Riechel, M., Matzinger, A., Pawlowsky-Reusing, E., Sonnenberg, H., Uldack, M., Heinzmann, B., Caradot, N., von Seggern, D., Rouault, P. (2016): Impacts of combined sewer overflows on a large urban river - Understanding the effect of different management strategies. Water Research 105: 264-273 10.1016/j.watres.2016.08.017</p> <p>Caradot, N., Sonnenberg, H., Riechel, M., Matzinger, A., Rouault, P. (2013): The influence of local calibration on the quality of UV-VIS spectrometer measurements in urban stormwater monitoring. Water Practice & Technology Vol 8 (No 3-4): 417-425 10.2166/wpt.2013.042</p> <p>Lepot, M., Torres, A., Hofer, T., Caradot, N., Gruber, G., Aubin, J.-B., Bertrand-Krajewski, J.-L. (2016): Calibration of UV/Vis spectrophotometers: A review and comparison of different methods to estimate TSS and total and dissolved COD concentrations in sewers, WWTPs and rivers. Water Research 101 (15 September 2016): 519-534 10.1016/j.watres.2016.05.070</p>
Roman sewer cover, Tarraco, Tarragona
Drain or sewer cover from the site of Tarraco (Tarragona, Spain). Marble drain cover that would have been in one of the rooms of the baths in a domus in the Tarraco port suburb area. 435 photos completely processed in Reality Capture. The project was done with the support of the National Archaeological Museum of Tarragona and the Agència Catalana del Patrimoni Cultural. Catalog No. MNAT Museu Nacional Arqueològic de Tarragona (2019): Tarraco: Exposición de síntesis. Generalitat de Catalunya Publicacions." Source: Objaverse 1.0 / Sketchfab
CoUDlabs_WP8_T811_UOS_001. Investigating geometrical effects on hydraulic energy losses during sewer to surface flow interactions during urban floods
<p>This document describes the dataset used in CO UD-labs JRA3 (WP 8) Task 8.1.1. This considers the hydraulic exchange (surcharge) from a piped drainage system to surface flood flow through a manhole. The dataset includes measurements of pressure, flow rate and depth from a physical scale model. The effect of changing the manhole lid properties on flow exchange (surcharge) and pressure in the experimental system is quantified over a range of flow rates. </p>
CoUDlabs_TA_USFD_02_ANNULAR_Regueiro. Time series analysis of sewer temperatures in an annular flume to estimate sediment accumulation
<p>This dataset contains the results of the experimental campaign and how data were collected on the the <a href="https://co-udlabs.eu/">Co-UDlabs</a> Transnational Access project: <em>Temperature time series analysis for predicting sedimentation in sewer systems</em>, by Regueiro-Picallo et al. The Transnational Access project was funded under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101008626.</p> <p>The experiments were designed to further develop an innovative methodology for measuring sediment bed deposits in UDS based on temperature data analysis (<a href="https://doi.org/10.1039/D2EW00820C" target="_blank" rel="noopener">Regueiro-Picallo et al., 2023</a>; <a href="https://doi.org/10.1039/D3EW00825H" target="_blank" rel="noopener">Regueiro-Picallo et al., 2024</a>). Particularly, the aim of these experiments was to study the influence of the sewer hydrodynamics for estimating the sediment accumulation using temperature-based methods. Therefore, the main tasks of this project were the design of a temperature-control system for the ANNULAR flume to reproduce sewer temperature patterns, and the development of an experimental campaign including different flow, sediment depth and temperature pattern conditions. The purpose of these measurements is to prove the use of simplified systems to monitor sediment accumulation in sewer systems.</p> <p>The data are described so that others can use and reproduce.</p>
Sewer Cover 3D scan
Photogrammetry Lowpoly model Source: Objaverse 1.0 / Sketchfab
Sewer mining modular unit at the Athens Urban Tree Nursery
<p>The Athens Urban Tree Nursery is part of the Goudi Park, an area in the process of redevelopment and regeneration to become the key metropolitan park of the capital. The area, which lies in the heart of Athens, is a mixed-use area, comprising of urban green and urban agriculture spaces as well as administration and residential uses. The regeneration is an effort to boost both the local economy and improve quality of life for the 4.000.000 citizens of the Attica Region.</p> <p>The nursery comprises 4 ha of vegetation, supplys all urban parks and green spaces of Athens with plant material and uses potable water from Athens’s Water Supply and Sewerage Company (EYDAP) for its irrigation. The city is seeking alternative water sources leveraging circular economy solutions to achieve environmental, social and financial benefits for the city.</p> <p>The installation of a sewer mining modular unit for urban green irrigation at the point of demand would be of direct benefit for the sustainability of the new metropolitan park. Additionally, compost-based eco-engineered growing media products will be reused as an onsite fertilizer, as part of a portfolio of autonomous, decentralized water, energy and materials circular solutions for cities in water scarce area. Finally, thermal energy recovery schemes will be investigated to minimize the pilot’s environmental footprint.</p>
Residual Life and Reliability Assessment of Underground RC sanitary Sewer Pipelines Under Uncertainty
<p>Prioritization of limited funding for pipelines maintenance is a major issue that concerns municipalities nationwide. Conducting a probabilistic assessment can provide a complete characterization of the performance of structural elements and systems along with optimizing the limited resources. The most widely used probabilistic performance indicator is reliability, a measure of the probability of failure corresponding to a particular limit state (e.g., ultimate strength or serviceability). Reliability methods can be used to identify which pipeline sections within a particular system require the most urgent inspection or repair. To this end, an automated data-driven framework for large diameter reinforced concrete pipes (RCPs) is developed that converts the raw unfiltered inspection readings to data that is used for estimating residual life and further reliability assessment purposes. In the current work, initially, the wall thickness erosion is determined based on the inspection data collected using Light Detection and Ranging (LiDAR). Furthermore, the best fit among several probability distribution functions for the wall thickness loss is obtained, which is integrated with a serviceability limit state that defines failure as the complete loss of 1-in concrete cover caused by environmental conditions such as sulfide-induced erosion. Considering this limit state and a prescribed probability of exceedance threshold, a reliability-based prediction of the remaining service life is proposed. The developed framework requires minimal user interference and is, therefore, less time consuming and more consistent compared to previous research. From an asset management point of view, the most vulnerable pipeline sections are identified that will require further inspection and attention. This will provide decision makers crucial information regarding the current state of the pipeline network, to better allocate the already scarce maintenance funding of these pipelines.</p>
Flow estimates example for k = 50 - Information-Theoretic Sensor Placement for Large-Scale Sewer Networks
<p>This dataset is used to generate Figure 7 without running the simulations in the paper Information-Theoretic Sensor Placement for Large-Scale Sewer Networks.</p>
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.