Case Studies Using RTC as a NMC and as a Stormwater BMP
Bibliographic record
Abstract
Real time controls (RTCs) are valued as a means to achieve the maximum capture of pollutants in combined sewer systems for meeting the EPA guideline of Nine Minimum Controls (NMC). Several communities also utilize RTCs to realize reductions in pollutant discharges from stormwater systems. This paper summarizes several of the examples where RTCs are being used as a cost effective combined sewer overflow control and/or stormwater pollutant reduction measure, i.e., as a stormwater best management practice (BMP). In combined sewer systems, real time controls are used to store peak flows in the sewers until treatment plant capacity becomes available, or to divert flows to less stressed portions of the sewer system, or to divert flows to storage facilities. In all cases, the net result is a reduction in the frequency and volume of combined sewer overflow, and hence an increase in the percentage of the stormwater runoff from the combined sewer service area that is captured and treated. The paper discusses several examples, concentrating on the case of Cornwall, Ontario where real time controls were retrofit, at minimal cost, to capture greater portions of the combined flow when stormwater increases the flow rates. Real time controls are less common in separated storm sewer systems, but they do exist. One example is in Edmonton, Canada where sensors are installed to monitor potentially toxic flows in an online oil removal facility. The contaminated stormwater is diverted to temporary storage if an accidental spill occurs on the roadway and disposed of appropriately when it is safe to do so. In another example, Milwaukee, WI is using an RTC system originally built for sanitary and combined sewer overflow control to effect capture of 40 percent of the total suspended solids in stormwater runoff from a proposed highway expansion. Other examples exist at airports to divert runoff contaminated with deicing chemicals to treatment while higher, less concentrated flows go to the storm sewer discharges. For each example, the paper presents the configuration of the RTC system, the costs allocated for stormwater pollution BMP portion, and the expected performance. To the extent possible, the studies and reasoning that led to selection of RTCs as a tool to improve capture and treatment of polluted stormwater are also summarized.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".