Global distribution of wastewater treatment plants and their released effluents into rivers and streams
Bibliographic record
Abstract
Abstract. The main objective of wastewater treatment plants (WWTPs) is to remove contaminants such as pathogens, nutrients, and organic and other pollutants from wastewaters using physical, biological and/or chemical processes prior to discharge into receiving waterbodies. However, since WWTPs cannot remove all contaminants, they inevitably represent concentrated point sources of residual contaminant loads into surface waters. To understand the severity and extent of the impact of wastewater discharges from such facilities into rivers and lakes, as well as to identify opportunities of improved management, detailed information about WWTPs is required, including (1) their explicit geospatial locations to identify the waterbodies affected; and (2) individual plant characteristics such as population served, flow rate of effluents, and level of treatment of processed wastewaters. These characteristics are especially important for contaminant fate models that are designed to assess the distribution of substances that are not typically included in environmental monitoring programs, such as contaminants of emerging concern. Although there are several regional datasets that provide information on WWTP locations and characteristics, data are still lacking at a global scale, especially in developing countries. Here we introduce HydroWASTE, a location-explicit global database of 58,502 WWTPs and their characteristics. This database was developed by combining national and regional datasets with auxiliary information to derive or complete missing WWTP characteristics, including the amount of people served. A high-resolution river network with streamflow estimates was used to georeference WWTP outfall locations and calculate each plant’s dilution factor (i.e., the ratio of the natural discharge of the receiving waterbody to the WWTP effluent discharge). The utility of this information was demonstrated in an assessment of the distribution of wastewaters at a global scale. Results show that 1.2 million kilometers of the global river network receive wastewater input from upstream WWTPs, of which more than 90,000 km are downstream of WWTPs that offer only primary treatment. Wastewater ratios originating from WWTPs exceed 10 % in over 72,000 km of rivers, mostly in areas of high population densities in Europe, USA, China, India, and South Africa. In addition, 2,533 plants show a dilution factor of less than 10, which represents a common threshold for environmental concern.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".