This proposed research focused on the prediction and identification of dissolved heavy metals in storm water runoff from elevated roadways. Storm water runoff from highways transports a significant load of contaminants, especially heavy metals and particulate matter, to receiving waters. Heavy metals, either in dissolved or particulatebound phases, are unique in the fact that unlike organic compounds, they are not degraded in the environment. The objective of this research was to develop a mathematical model to relate dissolved heavy metal concentration to different measurable parameters which are easily available and routinely measurable for elevated roadways. The reliability of the developed models was then evaluated by comparing the raw data versus data predicted by the models. The test site for this research was selected at the intersection of the Interstate-10 and Interstate-610, Orleans Parish, New Orleans, Louisiana. Subsequently a research test site was developed and highway storm water runoff was collected. Volumetric flow rates were measured with every collected sample by measuring the amount of collected water and the collection time. Storm water runoff from the examined elevated roadway section was sampled for 10 storm events throughout the course of the study from which hydrologic and water quality data were collected. The measurement of different parameters made it possible to determine the percentage of dissolved heavy metal mass loading and the characterization of high runoff flow intensity and low runoff flow intensity storm events. Another very important achievement in this research was the construction of a predictive model for dissolved heavy metal concentrations based on field measurements. Data analysis proceeded by applying different variable selection statistical methods as well as multiple regression analyses in order to evaluate the simultaneous effects of all variables on the concentration of dissolved heavy metals in storm water runoff. The developed model enables the user to predict dissolved heavy metal concentrations with known field measurements within a prediction interval of 95 % confidence. The reliability of the models was verified by carrying out significant-difference tests for both sets of data, observed and predicted, for a 5% of significance level.
| Identifer | oai:union.ndltd.org:uno.edu/oai:scholarworks.uno.edu:td-1154 |
| Date | 21 May 2005 |
| Creators | Erlacher, Ruben |
| Publisher | ScholarWorks@UNO |
| Source Sets | University of New Orleans |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | University of New Orleans Theses and Dissertations |
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