Engineered Bioremediation of NAPL Polluted Sites: Experimental and Simulation-Optimization Approach under Heterogeneous Moisture and Temperature Conditions
Bibliographic record
Abstract
An integrated experimental-numerical approach is used in this study to bioremediate a toluene, a nonaqueous phase liquid (NAPL), contaminated land site, having varying moisture and temperature levels. The rates of biodegradation in saturated and unsaturated zones under varying soil-moisture (100% to 20%) and temperature (30°C±2°C and 10°C±0.5°C) conditions are obtained by conducting a series of laboratory experiments first. Thereafter, an integrated approach for engineered bioremediation of a characteristic polluted subsurface site is developed considering a system of injection-extraction wells and a HYDRUS three-dimensional (3D) simulator. The injection-extraction wells system is optimally designed to enhance the natural bioremediation rate by having three injection wells and one extraction well to provide additional oxygen supply to the contaminated zone and to contain the NAPL plume in the treatment zone. The pumping rates for injection and extraction wells are optimized using an extreme learning machine-particle swarm optimization-based simulation-optimization approach (ELM-PSO). The results show that the biodegradation rates are high at 30°C for the polluted site having soil moisture content around field capacity. The degradation rate is reduced significantly at a lower temperature of 10°C, particularly when the soil moisture content is kept in the low (40%–20%) range. The designed injection-extraction well system shows that almost similar costs of remediation are required when the soil moisture content is maintained in the range of 60%–80% of the saturation level at a high (30°C) temperature. No substantial change in the time of remediation is observed by changing the soil moisture content between 60% and 80% at this high temperature. However, an elongated time period of treatment observed at a 60% moisture content as compared to the 80% level at a low (10°C) temperature indicates the dominant role of temperature stress as compared with the soil moisture availability. Further, this combination takes about 66% less time in remediating the pollutant concentration to an acceptable level than the time required at the low temperature and moisture content levels. The findings of this study are of direct use in planning remediation strategies for hydrocarbon contaminated sites.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".