Spatially-explicit estimates of global wastewater production,collection, treatment and re-use
Bibliographic record
Abstract
Abstract. Continually improving and affordable wastewater management provides opportunities for both pollution reduction and clean water supply augmentation, whilst simultaneously promoting sustainable development and supporting the transition to a circular economy. This study aims to provide the first comprehensive and consistent global outlook on the state of domestic and industrial wastewater production, collection, treatment and re-use. We use a data-driven approach, collating, cross-examining and standardising country-level wastewater data from online data resources. Where unavailable, data is estimated using multiple linear regression. Country-level wastewater data are subsequently downscaled and validated at 5 arc-minute (~ 10 km) resolution. This study estimates global wastewater production at 359.5 billion m3 yr−1, of which 63 % (225.6 billion m3 yr−1) is collected and 52 % (188.1 billion m3 yr−1) is treated. By extension, we estimate that 48 % of global wastewater production is released to the environment untreated, which is significantly lower than previous estimates of ~ 80 %. An estimated 40.7 billion m3 yr−1 of treated wastewater is intentionally re-used. Substantial differences in per capita wastewater production, collection and treatment are observed across different geographic regions and by level of economic development. For example, just over 16 % of the global population in high income countries produce 41 % of global wastewater. Treated wastewater re-use is particularly significant in the Middle East and North Africa (15 %) and Western Europe (16 %), while containing just 5.8 % and 5.7 % of the global population, respectively. Our database serves as a reference for understanding the global wastewater status and for identifying hotspots where untreated wastewater is released to the environment, which are found particularly in South and Southeast Asia. Importantly, our results also serve as a baseline for evaluating progress towards many policy goals that are both directly and indirectly connected to wastewater management (e.g. SDGs). Our spatially-explicit results available at 5 arc-minute resolution are well suited for supporting more detailed hydrological analyses such as water quality modelling and large-scale water resource assessments, and can be accessed at: https://doi.pangaea.de/10.1594/PANGAEA.918731 (Jones et al., 2020). A temporary link to this dataset for the review process can be accessed at: https://www.pangaea.de/tok/6631ef8746b59999071fa2e692fbc492c97352aa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".