Articles | Volume 14, issue 2
https://doi.org/10.5194/essd-14-559-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-559-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
09 Feb 2022
Data description article |
| 09 Feb 2022
| 09 Feb 2022
Distribution and characteristics of wastewater treatment plants within the global river network
Heloisa Ehalt Macedo
CORRESPONDING AUTHOR
Department of Geography, McGill University, Montreal, QC H3A 0B9,
Canada
Bernhard Lehner
CORRESPONDING AUTHOR
Department of Geography, McGill University, Montreal, QC H3A 0B9,
Canada
Jim Nicell
Department of Civil Engineering, McGill University, Montreal, QC H3A
2K7, Canada
Günther Grill
Department of Geography, McGill University, Montreal, QC H3A 0B9,
Canada
Jing Li
Department of Civil Engineering, McGill University, Montreal, QC H3A
2K7, Canada
Antonio Limtong
Department of Geography, McGill University, Montreal, QC H3A 0B9,
Canada
Ranish Shakya
Department of Geography, McGill University, Montreal, QC H3A 0B9,
Canada
Related authors
Heloisa Ehalt Macedo, Bernhard Lehner, Jim Nicell, and Günther Grill
Geosci. Model Dev., 17, 2877–2899, https://doi.org/10.5194/gmd-17-2877-2024, https://doi.org/10.5194/gmd-17-2877-2024, 2024
Short summary
Short summary
Treated and untreated wastewaters are sources of contaminants of emerging concern. HydroFATE, a new global model, estimates their concentrations in surface waters, identifying streams that are most at risk and guiding monitoring/mitigation efforts to safeguard aquatic ecosystems and human health. Model predictions were validated against field measurements of the antibiotic sulfamethoxazole, with predicted concentrations exceeding ecological thresholds in more than 400 000 km of rivers worldwide.
Juliette Bernard, Catherine Prigent, Carlos Jimenez, Etienne Fluet-Chouinard, Bernhard Lehner, Elodie Salmon, Philippe Ciais, Zhen Zhang, Shushi Peng, and Marielle Saunois
Earth Syst. Sci. Data, 17, 2985–3008, https://doi.org/10.5194/essd-17-2985-2025, https://doi.org/10.5194/essd-17-2985-2025, 2025
Short summary
Short summary
Wetlands are responsible for about a third of global emissions of methane, a potent greenhouse gas. We have developed the Global Inundation Extent from Multi-Satellites-MethaneCentric (GIEMS-MC) dataset to represent the dynamics of wetland extent on a global scale (0.25° × 0.25° resolution, monthly time step). This updated resource combines satellite data and existing wetland databases, covering 1992 to 2020. Consistent maps of other methane-emitting surface waters (lakes, rivers, reservoirs, rice paddies) are also provided.
Qing Ying, Benjamin Poulter, Jennifer D. Watts, Kyle A. Arndt, Anna-Maria Virkkala, Lori Bruhwiler, Youmi Oh, Brendan M. Rogers, Susan M. Natali, Hilary Sullivan, Amanda Armstrong, Eric J. Ward, Luke D. Schiferl, Clayton D. Elder, Olli Peltola, Annett Bartsch, Ankur R. Desai, Eugénie Euskirchen, Mathias Göckede, Bernhard Lehner, Mats B. Nilsson, Matthias Peichl, Oliver Sonnentag, Eeva-Stiina Tuittila, Torsten Sachs, Aram Kalhori, Masahito Ueyama, and Zhen Zhang
Earth Syst. Sci. Data, 17, 2507–2534, https://doi.org/10.5194/essd-17-2507-2025, https://doi.org/10.5194/essd-17-2507-2025, 2025
Short summary
Short summary
We present daily methane (CH4) fluxes of northern wetlands at 10 km resolution during 2016–2022 (WetCH4) derived from a novel machine learning framework. We estimated an average annual CH4 emission of 22.8 ± 2.4 Tg CH4 yr−1 (15.7–51.6 Tg CH4 yr−1). Emissions were intensified in 2016, 2020, and 2022, with the largest interannual variation coming from Western Siberia. Continued, all-season tower observations and improved soil moisture products are needed for future improvement of CH4 upscaling.
Bernhard Lehner, Mira Anand, Etienne Fluet-Chouinard, Florence Tan, Filipe Aires, George H. Allen, Philippe Bousquet, Josep G. Canadell, Nick Davidson, Meng Ding, C. Max Finlayson, Thomas Gumbricht, Lammert Hilarides, Gustaf Hugelius, Robert B. Jackson, Maartje C. Korver, Liangyun Liu, Peter B. McIntyre, Szabolcs Nagy, David Olefeldt, Tamlin M. Pavelsky, Jean-Francois Pekel, Benjamin Poulter, Catherine Prigent, Jida Wang, Thomas A. Worthington, Dai Yamazaki, Xiao Zhang, and Michele Thieme
Earth Syst. Sci. Data, 17, 2277–2329, https://doi.org/10.5194/essd-17-2277-2025, https://doi.org/10.5194/essd-17-2277-2025, 2025
Short summary
Short summary
The Global Lakes and Wetlands Database (GLWD) version 2 distinguishes a total of 33 non-overlapping wetland classes, providing a static map of the world’s inland surface waters. It contains cell fractions of wetland extents per class at a grid cell resolution of ~500 m. The total combined extent of all classes including all inland and coastal waterbodies and wetlands of all inundation frequencies – that is, the maximum extent – covers 18.2 × 106 km2, equivalent to 13.4 % of total global land area.
Heloisa Ehalt Macedo, Bernhard Lehner, Jim Nicell, and Günther Grill
Geosci. Model Dev., 17, 2877–2899, https://doi.org/10.5194/gmd-17-2877-2024, https://doi.org/10.5194/gmd-17-2877-2024, 2024
Short summary
Short summary
Treated and untreated wastewaters are sources of contaminants of emerging concern. HydroFATE, a new global model, estimates their concentrations in surface waters, identifying streams that are most at risk and guiding monitoring/mitigation efforts to safeguard aquatic ecosystems and human health. Model predictions were validated against field measurements of the antibiotic sulfamethoxazole, with predicted concentrations exceeding ecological thresholds in more than 400 000 km of rivers worldwide.
Cited articles
Agência Nacional de Águas (ANA): Atlas Esgotos: Despoluição
de bacias hidrográficas [data set], available at: https://metadados.snirh.gov.br/geonetwork/srv/por/catalog.search#/metadata/1d8cea87-3d7b-49ff-86b8-966d96c9eb01 (last access: September 2019), 2017.
Anderson, P. D., D'Aco, V. J., Shanahan, P., Chapra, S. C., Buzby, M. E.,
Cunningham, V. L., DuPlessie, B. M., Hayes, E. P., Mastrocco, F. J., Parke,
N. J., Rader, J. C., Samuelian, J. H., and Schwab, B. W.: Screening Analysis
of Human Pharmaceutical Compounds in U.S. Surface Waters, Environ. Sci.
Technol., 38, 838–849, https://doi.org/10.1021/es034430b, 2004.
Beusen, A. H. W., Van Beek, L. P. H., Bouwman, A. F., Mogollón, J. M., and Middelburg, J. J.: Coupling global models for hydrology and nutrient loading to simulate nitrogen and phosphorus retention in surface water – description of IMAGE–GNM and analysis of performance, Geosci. Model Dev., 8, 4045–4067, https://doi.org/10.5194/gmd-8-4045-2015, 2015.
Bunzel, K., Kattwinkel, M., and Liess, M.: Effects of organic pollutants
from wastewater treatment plants on aquatic invertebrate communities, Water
Res., 47, 597–606, https://doi.org/10.1016/j.watres.2012.10.031, 2013.
Central Pollution Control Board (CPCB): Inventorization of Sewage Treatment
Plants, available at:
http://nrcd.nic.in/writereaddata/FileUpload/NewItem_210_Inventorization_of_Sewage-Treatment_Plant.pdf (last access: October 2019), 2015.
Comisión Nacional del Agua (CONAGUA): Plantas de tratamiento de agua
residual (nacional), SINA [data set], available at:
http://sina.conagua.gob.mx/sina/tema.php?tema=plantasTratamiento (last
access: October 2019), 2018.
Daughton, C. G.: Real-time estimation of small-area populations with human
biomarkers in sewage, Sci. Total Environ., 414, 6–21, https://doi.org/10.1016/j.scitotenv.2011.11.015, 2012.
Daughton, C. G. and Ternes, T. A.: Pharmaceuticals and personal care
products in the environment: agents of subtle change?, Environ. Health
Perspect., 107, 907–938, https://doi.org/10.1289/ehp.99107s6907, 1999.
Department of Water and Sanitation (DWS): National Integrated Water Information System, available at: https://www.dws.gov.za/niwis2, last access: October 2019.
Dumont, E., Johnson, A. C., Keller, V. D. J., and Williams, R. J.: Nano
silver and nano zinc-oxide in surface waters – Exposure estimation for
Europe at high spatial and temporal resolution, Environ. Pollut., 196,
341–349, https://doi.org/10.1016/j.envpol.2014.10.022, 2015.
Ehalt Macedo, H., Lehner, B., Nicell, J., Grill, G., Li, J., Limtong, A.,
and Shakya, R.: HydroWASTE version 1.0., figshare [dataset], https://doi.org/10.6084/m9.figshare.14847786.v1, 2021.
Environment Canada: Wastewater Systems Effluent Regulations, WSER [data
set], available at:
https://www.canada.ca/en/environment-climate-change/services/wastewater/publications/wastewater-data-reports.html (last access: October 2019), 2017.
Environmental Medicines Agency (EMA): Guideline on the environmental risk
assessment of medicinal products for human use: available at:
https://www.ema.europa.eu/en/environmental-risk-assessment-medicinal-products-human-use#current-version-section (last access: December 2019), 2006.
European Environment Agency (EEA): Waterbase-UWWTD: Urban Waste Water
Treatment Directive – reported data, EEA [data set], available at:
https://www.eea.europa.eu/data-and-maps/data/waterbase-uwwtd-urban-waste-water-treatment-directive-6 (last access: October 2019), 2017.
Food and Agriculture Organization of the United Nations (FAO): AQUASTAT
Core Database, available at: https://www.fao.org/aquastat/en/databases/maindatabase/ (last access: October 2019), 2016.
Font, C., Bregoli, F., Acuña, V., Sabater, S., and Marcé, R.: GLOBAL-FATE (version 1.0.0): A geographical information system (GIS)-based model for assessing contaminants fate in the global river network, Geosci. Model Dev., 12, 5213–5228, https://doi.org/10.5194/gmd-12-5213-2019, 2019.
Grill, G., Khan, U., Lehner, B., Nicell, J., and Ariwi, J.: Risk assessment
of down-the-drain chemicals at large spatial scales: Model development and
application to contaminants originating from urban areas in the Saint
Lawrence River Basin, Sci. Total Environ., 541, 825–838, https://doi.org/10.1016/j.scitotenv.2015.09.100, 2016.
Grill, G., Li, J., Khan, U., Zhong, Y., Lehner, B., Nicell, J., and Ariwi,
J.: Estimating the eco-toxicological risk of estrogens in China's rivers
using a high-resolution contaminant fate model, Water Res., 145, 707–720,
https://doi.org/10.1016/j.watres.2018.08.053, 2018.
Grill, G., Lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli, F.,
Babu, S., Borrelli, P., Cheng, L., Crochetiere, H., Ehalt Macedo, H.,
Filgueiras, R., Goichot, M., Higgins, J., Hogan, Z., Lip, B., McClain, M.
E., Meng, J., Mulligan, M., Nilsson, C., Olden, J. D., Opperman, J. J.,
Petry, P., Reidy Liermann, C., Sáenz, L., Salinas-Rodríguez, S.,
Schelle, P., Schmitt, R. J. P., Snider, J., Tan, F., Tockner, K., Valdujo,
P. H., van Soesbergen, A., and Zarfl, C.: Mapping the world's free-flowing
rivers, Nature, 569, 215–221, https://doi.org/10.1038/s41586-019-1111-9, 2019.
Herrera, V.: Reconciling global aspirations and local realities: Challenges
facing the Sustainable Development Goals for water and sanitation, World
Dev., 118, 106–117, https://doi.org/10.1016/j.worlddev.2019.02.009, 2019.
Hill, R., Carter, L., and Kay, R.: Wastewater Treatment Facilities,
Geoscience Australia [data set], https://doi.org/10.4225/25/543B53F92E643, 2012.
Hofstra, N., Bouwman, A. F., Beusen, A. H. W., and Medema, G. J.: Exploring
global Cryptosporidium emissions to surface water, Sci. Total Environ., 442,
10–19, https://doi.org/10.1016/j.scitotenv.2012.10.013, 2013.
Jones, E. R., van Vliet, M. T. H., Qadir, M., and Bierkens, M. F. P.: Country-level and gridded estimates of wastewater production, collection, treatment and reuse, Earth Syst. Sci. Data, 13, 237–254, https://doi.org/10.5194/essd-13-237-2021, 2021.
Kapo, K. E., DeLeo, P. C., Vamshi, R., Holmes, C. M., Ferrer, D., Dyer, S.
D., Wang, X., and White-Hull, C.: iSTREEM®: An approach for
broad-scale in-stream exposure assessment of “down-the-drain” chemicals,
Integr. Environ. Assess. Manage., 12, 782–792, https://doi.org/10.1002/ieam.1793, 2016.
Keller, V. D. J., Williams, R. J., Lofthouse, C., and Johnson, A. C.:
Worldwide estimation of river concentrations of any chemical originating
from sewage-treatment plants using dilution factors, Environ. Toxicol.
Chem., 33, 447–452, https://doi.org/10.1002/etc.2441, 2014.
Kroeze, C., Gabbert, S., Hofstra, N., Koelmans, A. A., Li, A., Löhr, A.,
Ludwig, F., Strokal, M., Verburg, C., Vermeulen, L., van Vliet, M. T. H., de
Vries, W., Wang, M., and van Wijnen, J.: Global modelling of surface water
quality: a multi-pollutant approach, Curr. Opin. Env. Sust., 23, 35–45, https://doi.org/10.1016/j.cosust.2016.11.014, 2016.
Lehner, B. and Grill, G.: Global river hydrography and network routing:
baseline data and new approaches to study the world's large river systems,
Hydrol. Process., 27, 2171–2186, https://doi.org/10.1002/hyp.9740, 2013.
Lehner, B., Verdin, K., and Jarvis, A.: New global hydrography derived from
spaceborne elevation data, Eos, 89, 93–94, https://doi.org/10.1029/2008EO100001, 2008.
Link, M., von der Ohe, P. C., Voß, K., and Schäfer, R. B.:
Comparison of dilution factors for German wastewater treatment plant
effluents in receiving streams to the fixed dilution factor from chemical
risk assessment, Sci. Total Environ., 598, 805–813, https://doi.org/10.1016/j.scitotenv.2017.04.180, 2017.
Linke, S., Lehner, B., Ouellet Dallaire, C., Ariwi, J., Grill, G., Anand,
M., Beames, P., Burchard-Levine, V., Maxwell, S., Moidu, H., Tan, F., and
Thieme, M.: Global hydro-environmental sub-basin and river reach
characteristics at high spatial resolution, Sci. Data, 6, 283, https://doi.org/10.1038/s41597-019-0300-6, 2019.
Mayorga, E., Seitzinger, S. P., Harrison, J. A., Dumont, E., Beusen, A. H.
W., Bouwman, A. F., Fekete, B. M., Kroeze, C., and Van Drecht, G.: Global
Nutrient Export from WaterSheds 2 (NEWS 2): Model development and
implementation, Environ. Modell. Softw., 25, 837–853, https://doi.org/10.1016/j.envsoft.2010.01.007, 2010.
Messager, M. L., Lehner, B., Grill, G., Nedeva, I., and Schmitt, O.:
Estimating the volume and age of water stored in global lakes using a
geo-statistical approach, Nat. Commun., 7, 13603, https://doi.org/10.1038/ncomms13603, 2016.
Müller Schmied, H., Eisner, S., Franz, D., Wattenbach, M., Portmann, F. T., Flörke, M., and Döll, P.: Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration, Hydrol. Earth Syst. Sci., 18, 3511–3538, https://doi.org/10.5194/hess-18-3511-2014, 2014.
Munz, N. A., Burdon, F. J., de Zwart, D., Junghans, M., Melo, L., Reyes, M.,
Schönenberger, U., Singer, H. P., Spycher, B., Hollender, J., and Stamm,
C.: Pesticides drive risk of micropollutants in wastewater-impacted streams
during low flow conditions, Water Res., 110, 366–377, https://doi.org/10.1016/j.watres.2016.11.001, 2017.
Musolff, A., Leschik, S., Reinstorf, F., Strauch, G., and Schirmer, M.: Assessing emerging
contaminants – Case study of the city of Leipzig, Germany, IAHS-AISH
P., 178–185, 2008.
Nakada, N., Hanamoto, S., Jürgens, M. D., Johnson, A. C., Bowes, M. J.,
and Tanaka, H.: Assessing the population equivalent and performance of
wastewater treatment through the ratios of pharmaceuticals and personal care
products present in a river basin: Application to the River Thames basin,
UK, Sci. Total Environ., 575, 1100–1108, https://doi.org/10.1016/j.scitotenv.2016.09.180, 2017.
Neale, P. A., Munz, N. A., Aït-Aïssa, S., Altenburger, R., Brion, F., Busch,
W., Escher, B. I., Hilscherová, K., Kienle, C., Novák, J., Seiler,
T.-B., Shao, Y., Stamm, C., and Hollender, J.: Integrating chemical analysis
and bioanalysis to evaluate the contribution of wastewater effluent on the
micropollutant burden in small streams, Sci. Total Environ., 576, 785–795,
https://doi.org/10.1016/j.scitotenv.2016.10.141, 2017.
O'Brien, J. W., Thai, P. K., Eaglesham, G., Ort, C., Scheidegger, A.,
Carter, S., Lai, F. Y., and Mueller, J. F.: A Model to Estimate the
Population Contributing to the Wastewater Using Samples Collected on Census
Day, Environ. Sci. Technol., 48, 517–525, https://doi.org/10.1021/es403251g, 2014.
Oldenkamp, R., Hoeks, S., Čengić, M., Barbarossa, V., Burns, E. E.,
Boxall, A. B. A., and Ragas, A. M. J.: A High-Resolution Spatial Model to
Predict Exposure to Pharmaceuticals in European Surface Waters: ePiE,
Environ. Sci. Technol., 52, 12494–12503, https://doi.org/10.1021/acs.est.8b03862, 2018.
Rice, J. and Westerhoff, P.: Spatial and Temporal Variation in De Facto
Wastewater Reuse in Drinking Water Systems across the U.S.A, Environ. Sci.
Technol., 49, 982–989, https://doi.org/10.1021/es5048057, 2015.
Rice, J. and Westerhoff, P.: High levels of endocrine pollutants in US
streams during low flow due to insufficient wastewater dilution, Nat.
Geosci., 10, 587–591, https://doi.org/10.1038/ngeo2984, 2017.
Richter, B. D., Postel, S., Revenga, C., Scudder, T., Lehner, B., Churchill,
A., and Chow, M. J. W.: Lost in development's shadow: The downstream
human consequences of dams, Water Altern., 3, 14–42, 2010.
Strokal, M., Spanier, J. E., Kroeze, C., Koelmans, A. A., Flörke, M.,
Franssen, W., Hofstra, N., Langan, S., Tang, T., van Vliet, M. T. H., Wada,
Y., Wang, M., van Wijnen, J., and Williams, R.: Global multi-pollutant
modelling of water quality: scientific challenges and future directions,
Curr. Opin. Env. Sust., 36, 116–125, https://doi.org/10.1016/j.cosust.2018.11.004, 2019.
Superintendencia Nacional de Servicios de Saneamiento (SUNASS): Plantas de
Tratamiento de Agua Residual, GEOSUNASS [data set], available at:
https://geosunass.sunass.gob.pe/geoportal/sunass/home/index (last access: October 2019), 2018.
Tang, T., Strokal, M., van Vliet, M. T. H., Seuntjens, P., Burek, P.,
Kroeze, C., Langan, S., and Wada, Y.: Bridging global, basin and local-scale
water quality modeling towards enhancing water quality management worldwide,
Curr. Opin. Env. Sust., 36, 39–48, https://doi.org/10.1016/j.cosust.2018.10.004, 2019.
Tatem, A. J.: WorldPop, open data for spatial demography, Sci. Data,
4, 170004, https://doi.org/10.1038/sdata.2017.4, 2017.
Thiebault, T., Alliot, F., Berthe, T., Blanchoud, H., Petit, F., and Guigon,
E.: Record of trace organic contaminants in a river sediment core: From
historical wastewater management to historical use, Sci. Total Environ.,
773, 145694, https://doi.org/10.1016/j.scitotenv.2021.145694, 2021.
United Nations Environment Programme (UNEP): A Snapshot of the World's Water
Quality: Towards a global assessment, United Nations Environment Programme, Nairobi, Kenya, 162 pp., 2016.
United Nations Environment Programme – World Conservation Monitoring Centre (UNEP–WCMC) and International Union for Conservation of Nature (IUCN):
Protected Planet: The World Database on Protected Areas (WDPA), available at: https://www.iucn.org/theme/protected-areas/our-work/quality-and-effectiveness/world-database-protected-areas-wdpa, last access: January 2021.
United States Environmental Protection Agency (US EPA): Clean Watersheds
Needs Survey, EPA [data set], available at: https://www.epa.gov/cwns (last access:
December 2019), 2016.
Van Drecht, G., Bouwman, A. F., Harrison, J., and Knoop, J. M.: Global
nitrogen and phosphate in urban wastewater for the period 1970 to 2050,
Global Biogeochem. Cycles, 23, GB0A03, https://doi.org/10.1029/2009gb003458, 2009.
van Vliet, M. T. H., Flörke, M., Harrison, J. A., Hofstra, N., Keller,
V., Ludwig, F., Spanier, J. E., Strokal, M., Wada, Y., Wen, Y., and
Williams, R. J.: Model inter-comparison design for large-scale water quality
models, Curr. Opin. Env. Sust., 36, 59–67, https://doi.org/10.1016/j.cosust.2018.10.013, 2019.
van Vliet, M. T. H., Jones, E. R., Flörke, M., Franssen, W. H. P.,
Hanasaki, N., Wada, Y., and Yearsley, J. R.: Global water scarcity including
surface water quality and expansions of clean water technologies,
Environ. Res. Lett., 16, 024020, https://doi.org/10.1088/1748-9326/abbfc3,
2021.
Vigiak, O., Grizzetti, B., Zanni, M., Aloe, A., Dorati, C., Bouraoui, F.,
and Pistocchi, A.: Domestic waste emissions to European waters in the 2010s,
Sci. Data, 7, 33, https://doi.org/10.1038/s41597-020-0367-0, 2020.
Water New Zealand: New Zealand Wastewater Treatment Plant Inventory, WWTP [data set], available at: https://www.waternz.org.nz/WWTPInventory, last access: October 2019.
Williams, R., Keller, V., Voß, A., Bärlund, I., Malve, O.,
Riihimäki, J., Tattari, S., and Alcamo, J.: Assessment of current water
pollution loads in Europe: estimation of gridded loads for use in global
water quality models, Hydrol. Process., 26, 2395–2410, https://doi.org/10.1002/hyp.9427, 2012.
World Bank: Gross National Income (GNI) per capita, Atlas method, available at: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups,
last access: December 2019.
World Health Organization (WHO) and United Nations Habitat (UN Habitat):
Progress on safe treatment and use of wastewater: piloting the monitoring
methodology and initial findings for SDG indicator 6.3.1, WHO and UN-Habitat, Geneva, Switzerland, 40 pp., ISBN 978-9241514897, 2018.
World Health Organization(WHO) and United Nations Children's Fund (UNICEF): Joint Monitoring Programme (JMP) for water supply and sanitation
(WASH), available at: https://washdata.org/, last access: December 2019.
World Health Organization (WHO) and United Nations Children's Fund (UNICEF): Progress on household drinking water, sanitation and hygiene
2000–2020: five years into the SDGs, WHO and UNICEF, Geneva, Switzerland, 164 pp., ISBN (WHO) 978-9240030848, 2021.
WorldPop and Center for International Earth Science Information Network (CIESIN): Global High Resolution Population Denominators Project, https://doi.org/10.5258/SOTON/WP00647, 2018.
Short summary
We introduce HydroWASTE, a spatially explicit global database of 58 502 wastewater treatment plants (WWTPs) and their characteristics to understand the impact of discharges from such facilities. HydroWASTE was developed by compiling regional datasets and using auxiliary information to complete missing characteristics. The location of the outfall of the WWTPs into the river system is also included, allowing for the identification of the waterbodies most likely affected.
We introduce HydroWASTE, a spatially explicit global database of 58 502 wastewater treatment...
Altmetrics
Final-revised paper
Preprint