Date of this Version
Klein, M. (2013). Passive Stormwater Samplers for Sampling Highway Runoff from BMPs: Feasibility Studies. M.S. Thesis, Department of Civil Engineering, Univ. of Nebraska, Lincoln, NE.
Pollution from highway stormwater runoff has been an increasing area of concern within the environmental field. To respond to the need for reduced contamination within runoff many Best Management Practices (BMPs) have been implemented. One difficult aspect of BMPs is monitoring their effectiveness along with determining effluent concentrations. The current methods for stormwater sampling include sending technicians or installing an auto-sampler to collect either grab or composite samples. These methods become costly, cumbersome and infeasible due to the potentially large amount of BMPs across a region and the irregularity and difficulty of predicting storms. Passive samplers have proven themselves as reliable and cost-effective for the measurement of groundwater, seawater and air pollution; but a greater understanding is needed for application within stormwater monitoring conditions.
The objective of this research is to develop a passive sampler that will operate under roadside BMP conditions and test its feasibility for BMP stormwater sampling. Nineteen existing groundwater passive samplers have been reviewed for possible use in stormwater scenarios along with three sorbents for heavy metal monitoring. From these, two have been selected for batch tests analyzing the kinetic uptake of these samplers. Further testing includes the use of lab-scale BMPs with differing loading scenarios for synthetic storms and sampler deployment within field BMPs.
Batch test results reveal ion exchange resin as a potential sorbent unhindered by stormwater matrix effects (i.e. the addition of sediments) and able to have fast contaminant uptake, while regenerated cellulose samplers proved infeasible. Lab-scale and field results show a variety of unforeseen factors that hinder the predictable uptake of metals onto the passive samplers in BMPs scenarios.
Advisor: Tian C. Zhang