Urban stormwater quality data collected over the past 20 years for several large government-sponsored sampling programs in the United States were assembled and analyzed to develop new nationwide estimators and statistics for urban storm water quality. We believe that this is the first attempt to assemble and analyze these major storm water quality data sets for this purpose. In this paper, the first public report of our work to-date, we present the results of the data acquisition, data base assembly, quality assurance, computation of new stormwater event mean concentrations and associated statistics, and comparisons with the original U.S. Environmental Protection Agency's Nationwide Urban Runoff Program (NURP) results. The differences between the pooled means and those estimated from our analysis of the NURP data range from a 79% lower estimate for Copper to a 36% higher estimate for Biochemical Oxygen Demand. It is concluded that the variations between the NURP results and those developed here from the pooling of the three national data bases are important and that future work may provide a basis for differentiating Event Mean Concentrations among urban land uses, geographic region and seasons.

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Citizen outcry in the 1960s led to passage of the 1972 U.S. Clean Water Act. Expansion of industrial permitting and availability of federal grants to municipalities controlled industrial waste and untreated municipal sewage entering the Rouge River. However, many sources persisted – notably wet weather discharges, stormwater runoff, and contaminated sediments. This remaining pollution led state officials to cooperatively craft the Rouge River Remedial Action Plan in 1985. This plan addressed all pollution sources, but was not substantially implemented until 1993 when the federal government, encouraged by Congressmen Dingell and Knollenberg, committed to the Rouge River National Wet Weather Demonstration Project. The federal government ultimately delivered $350 million that was matched by $700 million in local funds. Efforts have been sustained through multi-year state and federal grants, with additional funding from local communities and other stakeholders. Early focus of the Rouge Project was on untreated sewage from combined sewer overflows, but quickly expanded to address other impairments from sanitary sewer overflows, stormwater runoff, illicit connections and failing septic systems. With major sewage discharges under control, efforts shifted to remediating contaminated sediments and improving in-stream water quality and habitat. In total, over 380 projects were completed by 75 communities and agencies at a cost of over $1 billion since 1988, resulting in improved water, sediment, and biological quality. Prior to the U.S. Clean Water Act, the Rouge River nearly continuously failed to meet water quality standards. After decades of effort and investment, it now rarely violates standards. This miraculous recovery was initiated by a small handful of citizens, facilitated by local municipal leaders, and supported by the federal government. The Rouge River is a model for how a holistic, ecosystem approach to water pollution can result in cost-effective and greater and faster achievement of restoration, while meeting local needs.

A “user‐friendly” computer program has been developed for application in personal computers for preliminary design, evaluation, and cost effectiveness analysis of various best management practice (BMP) measures to control stormwater quantity and quality. The algorithms utilize the SCS TR‐55 method for calculating runoff hydrographs for a single storm event and a first order pollutant washoff equation to generate pollutographs. Sensitivity analyses based on different policy scenarios is performed on a hypothetical watershed for the purpose of illustration. Three types of BMP measures, namely detention ponds (dry, wet, and extended wet ponds), infiltration trenches, and porous pavements are considered. It is found that the extended wet ponds have the best cost effective performance of the measures evaluated.

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