In November of 2003, the U.S. Environmental Protection Agency (EPA) published a draft policy intended to clarify NPDES permit conditions for peak wet weather flow scenarios at publicly-owned treatment works (POTWs). The blending policy, as it is commonly known, dealt specifically with
situations where a portion of peak wet weather flow that has received the equivalent of primary treatment is routed around biological treatment units, blended with biologically-treated effluent, and discharged meeting effluent limits based on secondary treatment standards. Permitting of such
scenarios has not been consistent among regions and delegated states, with some authorities interpreting blending as an illegal bypass. Consent orders and other enforcement actions against various CSO and SSO communities have placed great emphasis on biological treatment; in some cases, POTWs
serving separate sanitary sewers have been required to provide biological treatment for 100% of the flows reaching the plant, leading to significant capital expenditures for treatment schemes with dubious environmental and public health benefits. The impetus behind the blending policy was
to provide a framework for reducing discharges of untreated wastewater, while at the same time ensuring that blending is limited in application and not used as an alternative to investment in wastewater infrastructure. EPA received more than 98,000 comments on the proposed policy. Many
of these comments were form letters opposing the policy on general principles. There were also many comments that were supportive of EPA's goals but identified technical issues that, in the commenters' view, the proposed policy did not adequately address. It was asserted that the pathogen
levels in blended effluents, in particular viruses and parasites such as Cryptosporidium sp. and Giardia sp., were generally higher than those receiving full biological treatment, and that the resultant public health risks could be unacceptable even when these effluents met existing
water quality-based effluent limitations for indicator bacteria. Those making this assertion believe that biological treatment provides more effective removal of pathogens than primary sedimentation or other physical processes. Further, higher levels of suspended solids and turbidity in primary
versus secondary effluent were identified as contributing to reduced efficiency of both chemical and irradiative disinfection processes. Some commenters recommended that EPA perform a quantitative microbial risk analysis to determine the public health impacts of the blending policy before
adopting it in its final form. Such an analysis would require reasonable estimates of pathogen densities in both blended effluents and biologically treated effluents, so that the relative risks could be compared. These estimates would need to account for the incidental removal of pathogens
in unit processes, and also characterize the process effluent quality as it relates to the efficiency of downstream disinfection processes. In particular, consideration should be given as to what extent the general benefits of biological treatment are still present under the stress of peak
wet weather flows. This paper will briefly summarize the comments on the proposed blending policy that pertain to pathogen loads in blended discharges. It will then describe a general framework for models of pathogen removal in unit processes that can be used to drive an exposure-based
risk assessment. An overview of the adequacy of existing data to address pathogen concerns will be provided. The paper will describe some of the mechanisms of biological treatment that may be responsible for enhancing the efficiency of downstream disinfection processes, and consider how peak
wet weather flows affect these mechanisms. The ability of other unit processes to achieve similar effects will also be discussed. Quantitative examples will be provided that compare estimated pathogen levels between various wastewater treatment scenarios, including blending. Suggestions will
be offered for directions of further research that would address the data gaps in the present analysis.
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