Environmental impacts of using desalinated water in concrete production in areas affected by freshwater scarcity
Bibliographic record
Abstract
Up to 500 litres of water may be consumed at the batching plant per cubic meter of ready mix concrete, if water for washing mixing trucks and equipment is included. Demand for concrete is growing almost everywhere, regardless of local availability of freshwater. The use of freshwater for concrete production exacerbates stress on natural water resources. In water-stressed coastal countries such as Israel, desalinated seawater (DSW) is often used in the production of concrete. However, the environmental impacts of this practice have not yet been assessed. In this study the effect of using DSW on the water and carbon footprints of concrete was investigated using life cycle assessment. Water footprint results highlight the benefits of using DSW rather than freshwater to produce concrete in Israel. In contrast, because desalination is an energy intensive process, using DSW increases the greenhouse gas intensity of concrete. Nevertheless, this increase (0.27 kg CO2e/m3 concrete) is small, if compared to the life cycle greenhouse gas emissions of concrete. Our results show that using untreated seawater in the mix (transported by truck from the coast) in place of DSW, would be beneficial in terms of water and carbon footprints if the batching plant were located less than 13 km from the withdrawal point. However, use of untreated seawater increases steel reinforcement corrosion, resulting in loss of structural integrity of the reinforced concrete composite. Sustainability of replacing steel with non-corrosive materials should be explored as a way to reduce both water and carbon footprints of concrete.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".