Impact of Green Infrastructures for Stormwater Volume Reduction in Combined Sewers: A Statistical Approach for Handling Field Data from Paired Sites Containing Rain Gardens and Planter Boxes
Bibliographic record
Abstract
Combined sewer overflow (CSO) is one of the major water pollution problems faced by many municipalities that are on combined sewer systems. Green infrastructures (GIs) can help mitigate urban stormwater problems including CSOs. A field-based study over 3 years investigated the effectiveness of rain gardens and planter boxes as GIs for stormwater control in residential urban areas. The objectives of this study were to (1) investigate the impact of field-scale GIs on stormwater volume reduction in combined sewers at paired sites in an urban area, and (2) develop a methodology, including the application of a series of statistical methods, for analyzing field-based data and addressing the variations of field-collected data. The paired sites were both located in the City of St. Louis, Missouri. The test site had 12 rain gardens and six planter boxes, whereas the control site had no GIs. The stormwater was separated from the combined flows measured in sewers using the antecedent subsequent dry-days estimation method and were then normalized by drainage area and rainfall amount. The statistical methods deployed for this study included the Anderson Darling normality test, the Mann-Whitney U-test, and the Monte Carlo randomization test. This study revealed that, based on the normalized stormwater volume in the sewers from all of acceptable rainfall events, the means were 0.409±0.356 m3/m2-m (0.255±0.222 gal./sqft-in.) at the test site and 0.704±0.979 m3/m2-m (0.439±0.610 gal./sqft-in.) at the control site, revealing a 42% reduction of stormwater; the medians were 0.321 m3/m2-m (0.200 gal./sqft-in.) at the test site and 0.428 m3/m2-m (0.267 gal./sqft-in.) at the control site, revealing a 25% reduction of stormwater. For small rainfall events, the reduction was higher, at 62%. The methodology developed in this study can be used for other field-based studies. The findings provide needed information for the application of GIs to manage stormwater to help reduce CSOs.
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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 | 0.002 | 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".