Mineral Adsorbents for Removal of Metals in Urban Runoff
Bibliographic record
Abstract
The aim of this research was to determine the capacity of four different soil minerals to adsorb metals frequently detected in urban runoff. These are low-cost, natural and commercially available soil minerals. Contaminated surface runoff from urban areas is a major cause of concern for water quality and aquatic ecosystems worldwide. Pollution in urban areas is generated by a wide array of non-point sources, including vehicular transportation and building materials. Some of the most frequently detected pollutants in urban runoff are metals. Exhaust gases, tire wear and brake linings are major sources of such metals as Pb, Zn and Cu, while impregnated wood, plastics and galvanized surfaces may release As, Cd, Cr and Zn. Many metals have toxic effects on aquatic plants and animals, depending on metal speciation and bioavailability. The removal efficiency of pollutants in stormwater depends on treatment practices and on the properties the pollutant. The distribution of metals in urban runoff has shown, for example, that Pb is predominantly particle-associated, whereas Zn and Cd are present mainly in dissolved form. Many metals are also attached to colloids, which may act as carriers for contaminants, thereby facilitating their transport through conventional water treatment processes. Filtration of stormwater is one of the most promising techniques for removal of particulates, colloidal and truly dissolved pollutants, provided that effective filtration and adsorption media are used. Filtration and infiltration are used in a wide array of stormwater treatment methods e.g. porous paving, infiltration drains and rain gardens. Several soil minerals were investigated for their potential as stormwater filter materials. Laboratory batch tests were conducted to determine the adsorption capacity of these minerals. A synthetic stormwater was tested, with spiked concentrations corresponding to levels reported in urban runoff, ranging from 50–1,500 µg/L for Zn; 5–250 µg/L for Cu, 2–20 µg/L for Cd and 10–150 µg/L for Ni and Pb. Humic acids were used to imitate natural stormwater contaminated with natural organic matter (NOM). The adsorption kinetics was also investigated through a sequence (10–120 min) of batch tests. By studying the capacity of a range of sorbents in batch tests under identical conditions, the most promising sorbent can be identified. The research is ongoing. Preliminary results will be presented.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".