The growing impact of urban stormwater on surface‐water quality has illuminated the need for more accurate modeling of stormwater pollution. Water quality based regulation and the movement towards integrated urban water management place a similar demand for improved stormwater quality model predictions. The physical, chemical, and biological processes that affect stormwater quality need to be better understood and simulated, while acknowledging the costs and benefits that such complex modeling entails. This paper reviews three approaches to stormwater quality modeling: deterministic, stochastic, and hybrid. Six deterministic, three stochastic, and three hybrid models are reviewed in detail. Hybrid approaches show strong potential for reducing stormwater quality model prediction error and uncertainty. Improved stormwater quality models will have wide ranging benefits for combined sewer overflow management, total maximum daily load development, best management practice design, land use change impact assessment, water quality trading, and integrated modeling.

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Biological indicators are used, in part, to assess the level of water quality with respect to this general standard. Under EPA's Total Maximum Daily Load (TMDL) program, impaired waters based on a biological assessment require an additional step compared with non-biological TMDLs. In non-biological TMDLs, the "pollutant" is typically the parameter being monitored, with a direct link to the impairment. In biological TMDLs, cause and effect must first be established between one or more pollutants and the impacted biological community. This article presents examples of approaches taken in different states to monitor and assess the biological health of our streams based on varying combinations of algal, macroinvertebrate, and fish communities. While fish are the ultimate integrator of lower ecological organisms, their occurrence and abundance has been greatly manipulated by humankind. Periphytic algae are perhaps the fastest responding biological population and can be used for some pollutant-specific diagnoses, but most states lack the expertise required for detailed taxonomic classification. Macroinvertebrates, the most commonly monitored biological community, are abundant in most streams, but most metrics are not diagnostic of specific stressors. Within the TMDL framework, issues are discussed related to setting TMDL targets, linking biological impairments with pollutants, and defining biological target endpoints. Although surrogate measures are often used for setting TMDL target loads, biological recovery is measured against biological endpoints. The use of biological indicators for assessment and development of biological TMDLs can be improved through modeling procedures that better define cause-and-effect relationships, through a better understanding of the limits of restoration, and through a more unified national policy that focuses on restoration.

Environmental decision support systems (EDSSs) are an emerging tool used to integrate the evaluation of highly complex and interrelated physicochemical, biological, hydrological, social, and economic aspects of environmental problems. An EDSS approach is developed to address hot-spot concerns for a water quality trading program intended to implement the total maximum daily load (TMDL) for phosphorus in the Non-Tidal Passaic River Basin of New Jersey. Twenty-two wastewater treatment plants (WWTPs) spread throughout the watershed are considered the major sources of phosphorus loading to the river system. Periodic surface water diversions to a major reservoir from the confluence of two key tributaries alter the natural hydrology of the watershed and must be considered in the development of a trading framework that ensures protection of water quality. An EDSS is applied that enables the selection of a water quality trading framework that protects the watershed from phosphorus-induced hot spots. The EDSS employs Simon's (1960) three stages of the decision-making process: intelligence, design, and choice. The identification of two potential hot spots and three diversion scenarios enables the delineation of three management areas for buying and selling of phosphorus credits among WWTPs. The result shows that the most conservative option entails consideration of two possible diversion scenarios, and trading between management areas is restricted accordingly. The method described here is believed to be the first application of an EDSS to a water quality trading program that explicitly accounts for surface water diversions.

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Car wash runoff is known to be a pollution source to surface water bodies. Many groups hold car-washing fundraisers unaware of pollution issues associated with car wash runoff. This preliminary study investigated whether rain gardens are an appropriate management practice for reducing car wash pollutants, specifically surfactants. The concentrations of total phosphorus (TP), total suspended solids (TSS), and surfactants were measured in car wash runoff before and after treatment in three rain garden mesocosms. Mean TSS and surfactant effluent concentrations were significantly lower than the car wash runoff with TSS reductions ranging from 84 to 95% and surfactant reductions ranging from 89 to 96%. The removal efficiencies for surfactants were not enough to reduce concentrations below literature-based values for aquatic toxicity. Mean TP effluent concentrations were higher than the car wash runoff with increases ranging from 197 to 388%, although the increase was not statistically significant. This project demonstrates the potential for using bioretention to reduce pollutants associated with car wash runoff and using car wash events to educate the public about watershed protection.

Kardos, Josef S. and Christopher C. Obropta, 2011. Water Quality Model Uncertainty Analysis of a Point‐Point Source Phosphorus Trading Program. Journal of the American Water Resources Association (JAWRA) 47(6):1317–1337. DOI: 10.1111/j.1752‐1688.2011.00591.x Abstract:  Water quality modeling is a major source of scientific uncertainty in the Total Maximum Daily Load (TMDL) process. The effects of these uncertainties extend to water quality trading programs designed to implement TMDLs. This study examines the effects of water quality model uncertainty on a nutrient trading program. The study builds on previous work to design a phosphorus trading program for the Nontidal Passaic River Basin in New Jersey that would implement the watershed TMDL for total phosphorus (TP). The study identified how water quality model uncertainty affects outcomes of potential trades of TP between wastewater treatment plants. The uncertainty analysis found no evidence to suggest that the outcome of trades between wastewater treatment plants, as compared with command and control regulation, will significantly increase uncertainty in the attainment of dissolved oxygen surface water quality standards, site‐specific chlorophyll a criteria, and reduction targets for diverted TP load at potential hot spots in the watershed. Each simulated trading scenario demonstrated parity with or improvement from the command and control approach at the TMDL critical locations, and low risk of hot spots elsewhere.