Stormwater Runoff Treatment Using Pervious Concrete Modified with Various Nanomaterials: A Comprehensive Review
Bibliographic record
Abstract
Clean water is a vital need for all living creatures during their lifespan. However, contaminated stormwater is a major issue around the globe. A wide range of contaminants, including heavy metals, organic and inorganic impurities, has been discovered in stormwater. Some commonly utilized methods, such as biological, physical and chemical procedures, have been considered to overcome these issues. However, these current approaches result in moderate to low contaminant removal efficiencies for certain classes of contaminants. Of late, filtration and adsorption processes have become more featured in permeable concretes (PCs) for the treatment of stormwater. As nanoparticles have vast potential and unique characterizations, such as a higher surface area to cure polluted stormwater, employing them to improve permeable concretes’ capabilities in stormwater treatment systems is an effective way to increase filtration and adsorption mechanisms. The present study reviews the removal rate of different stormwater contaminants such as heavy metals, organic and other pollutants using nanoparticle-improved PC. The application of different kinds of nanomaterials in PC as porous media to investigate their influences on the properties of PC, including the permeability rate, compressive strength, adsorption capacity and mix design of such concrete, was also studied. The findings of this review show that different types of nanomaterials improve the removal efficiency, compressive strength and adsorption capacity and decrease the infiltration rate of PC during the stormwater treatment process. With regard to the lack of comprehensive investigation concerning the use of nanomaterials in PC to treat polluted stormwater runoff, this study reviews 242 published articles on the removal rate of different stormwater contaminants by using PC improved with nanoparticles.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".