Leu-M

The low impact development (LID) approach has been recommended as an alternative to traditional stormwater design. Research on individual LID practices such as bioretention, pervious pavements, and grassed swales has increased in recent years. Bioretention cells have been effective in retaining large volumes of runoff and pollutants on site, and consistently reduced concentrations of certain pollutants such as metals. However, retention of certain pollutants such as nitrate–nitrogen and phosphorus has been problematic. Porous pavements have been extremely effective in infiltrating stormwater runoff. Concerns have been raised about groundwater contamination, but research has shown that this is not a problem in most settings. Green roofs have been found to retain a large percentage of rainfall (63% on average) in a variety of climates. A common thread across bioretention, green roofs and grassed swales was found: the export of phosphorus. The issue appears to be linked to high phosphorus levels in the soil media, or possibly to fertilization of turf or planted areas. Solutions to this problem have been recommended. Contrary to popular belief, research has shown that bioretention and pervious pavements continue to infiltrate even with frost in the ground. Although issues have been identified with retention of certain pollutants, the LID approach has been found to result in increased retention of stormwater and pollutants on site, mimicking pre-development hydrologic function. Future research needs have also been identified.

https://clear.uconn.edu/projects/tmdl/library/papers/dietz_2007.pdf

Low Impact Development Practices: A Review of Current Research and Recommendations for Future Directions

Understanding, management and modelling of urban hydrology and its consequences for receiving waters: A state of the art

DISCLAIMER The use of company or product name(s) is for identification only and does not imply endorsement by the Agency for Toxic Substances and Disease Registry. A toxicological Profile for PCBs, Draft for Public Comment, was released in December 1998. This edition supercedes any previously released draft or final profile. Toxicological profiles are revised and republished as necessary, but no less than once every three years. The toxicological profiles are developed in response to the Superfund Amendments and Reauthorization Act (SARA) of 1986 (Public law 99-499) which amended the Comprehensive Environmental Response, Compensation, and Liability Act of 1980 (CERCLA or Superfund). This public law directed ATSDR to prepared toxicological profiles for hazardous substances most commonly found at facilities on the CERCLA National Priorities List and that pose the most significant potential threat to human health, as determined by ATSDR and the EPA. The availability of the revised priority list of 275 hazardous substances was announced in the Administrator of ATSDR to prepare a toxicological profile for each substance on the list. Toxicological Profiles are a unique compilation of toxicological information on a given hazardous substance. Each profile reflects a comprehensive and extensive evaluation, summary, and interpretation of available toxicologic and epidemiologic information on a substance. Health care providers treating patients potentially exposed to hazardous substances will find the following information helpful for fast answers to often-asked questions. Chapter 1: Public Health Statement: The Public Health Statement can be a useful tool for educating patients about possible exposure to a hazardous substance. It explains a substance's relevant toxicologic properties in a nontechnical, question-and-answer format, and it includes a review of the general health effects observed following exposure. Chapter 3: Health Effects: Specific health effects of a given hazardous compound are reported by type of health effect (death, systemic, immunologic, reproductive), by route of exposure, and by length of exposure (acute, intermediate, and chronic). In addition, both human and animal studies are reported in this section. NOTE: Not all health effects reported in this section are necessarily observed in the clinical setting. Please refer to the Public Health Statement to identify general health effects observed following exposure. The following additional material can be ordered through the ATSDR Information Center: Case Studies in Environmental Medicine: Taking an Exposure History—The importance of taking an exposure history and how to conduct one are described, and an example of a thorough exposure history is provided. Other …

Toxicological profile for polychlorinated biphenyls (PCBs)

https://www.sheffield.ac.uk/polopoly_fs/1.149361!/file/gprgpaper_2005_wakida_lerner.pdf

Non-agricultural sources of groundwater nitrate: a review and case study.

Storm water quality can have a significant impact on receiving water bodies. Two of the major impacts can be to aquatic Ufe in the receiving water body and downstream water users that might use the water as a raw water source for drinking water. With increased understanding of the importance of drinking water quality to public health and the recognition that protecting raw water sources is an integral component of the water treatment process, there is a greater need to identify possible contaminants found in storm water that impact the receiving water quality. Presented is a literature review to identify and quantify contaminant data available on storm water. The article focuses on work that presented specific chemical, physical, and biological parameters rather than the traditionally used overall water quality parameters, such as biochemical oxygen demand and total suspended solids. To assess impacts and to put into perspective the importance of the reported concentrations, values were compared ...

Urban stormwater quality: Summary of contaminant data

Forecasting raw-water quality parameters for the North Saskatchewan River by neural network modeling

To improve drinking water quality while reducing operating costs, many drinking water utilities are investing in advanced process control and automation technologies The use of artificial intellig

Drinking water quality and treatment: the use of artificial neural networks

Described is the development of a full-scale artificial neural network (ANN) model for the removal of natural organic matter (NOM) by enhanced coagulation at the Rossdale Water Treatment Plant (WTP) in Edmonton, Alberta, Canada. Few attempts have been made to develop a full-scale model of the enhanced coagulation process due to extreme variability in the process parameters and the complex nonlinear relationships between them. When applied to previously unseen data, the model predicted effluent colour with a high degree of accuracy. The model will be incorporated into real-time process control at the WTP following a period of online testing.

Development of a full-scale artificial neural network model for the removal of natural organic matter by enhanced coagulation

Mathematical models previously employed to analyze concentration profiles of dissolved ozone in bubble‐column contactors often assumed either plug flow or complete mixing. In this paper a more realistic model, the axial dispersion model (ADM), was provided to integrate the nonideal mixing, mass‐transfer, and ozone‐decay process as a whole. The effects of various process parameters on the ozone profiles were analyzed numerically. It was found that a group of dimensionless quantities (PeL, Da, StG, and StL) is most convenient in describing the performance of ozone‐contacting systems. The different types of ozone contactors (countercurrent‐flow bubble columns, cocurrent‐flow bubble columns, and reactive contacting chambers) exhibited different patterns of concentration profile; as a result, different approaches are needed to relate their characteristic concentrations to readily observed measurements for predicting the disinfection efficiency. The applicability of the model is illustrated through the analysis...

Stephen J. Stanley

Papers

Urban stormwater quality: Summary of contaminant data

Forecasting raw-water quality parameters for the North Saskatchewan River by neural network modeling

Drinking water quality and treatment: the use of artificial neural networks

Development of a full-scale artificial neural network model for the removal of natural organic matter by enhanced coagulation

Modeling of Dissolved Ozone Concentration Profiles in Bubble Columns