Statistical evaluation of United States EPA region 6 risk model for stormwater impacts of construction activities in Grand Prairie, Texas

Risk evaluations are playing an increasingly important role in determining environmental policies and decisions at the U.S. Environmental Protection Agency (EPA). Computer modeling of Stormwater systems is currently achieving a high degree of sophistication and complexity. The complex models provide very valuable data for the design and final sizing of Stormwater facilities. There is a need for a framework for characterizing risks that involve numerous stressors, interconnected pathways, and multiple endpoints. As a result, a watershed-based screening tool, the Cumulative Risk Index Analysis (CRIA), was developed by U.S. EPA Region 6 in 1992 to assess the Stormwater quality in a watershed subunit. The existing CRIA criteria were often selected based upon judgment rather than scientific evidence or direct comparison to observed water quality data. The purpose of this study was to evaluate the current choices of CRIA criteria and suggest alternative, more appropriate ones to assess the impacts on Stormwater quality due to construction activities in the City of Grand Prairie. This research includes validation of the proposed criteria by conducting rigorous statistical analysis to validate the relationship between CRIA score and in-stream turbidity data collected within the corresponding area of study. The inclusion of the proposed criteria in the CRIA model shows a correlation coefficient of 0.972, a significant improvement over the previous model that showed a correlation of 0.0295. The 8 proposed (modified) CRIA criteria represent an improvement in the current methodology as the analysis is much more scientific, objective and has stronger statistical data support. This study demonstrates the application of an inexpensive screening tool to identify vulnerable monitoring areas in urban or urbanizing watersheds in Grand Prairie, Texas and thus provide objectively generated assessments to the decision-makers.