Process for Treatment BMP Selection for a Solid Waste Facility
This paper and presentation will follow a solid waste facility, focusing on the process of selecting/implementing a passive and/or active stormwater treatment Best Management Practice (BMP) in Santa Clara, CA. Solid waste facilities are unique as they each differentiate through source type, industrial process, material load as well as the collection and conveyance of industrial stormwater and process water. It is through these factors that makes the treatment BMP selection process most daunting. This case study will assess the elements of treatment feasibility that the Facility took in order to select an appropriate stormwater treatment BMP.
The California Industrial General Permit (IGP) became effective on July 1, 2015. Industrial facilities
across the state are facing continuous challenges with: Total Suspended Solids (TSS), various total metals
including zinc, copper, aluminum and iron as well as Chemical Oxygen Demand (COD). It is through the
IGP Numeric Action Levels (NALs) of these parameters that industrial facilities are failing. This facility in specific, was driven through early-action by a Non-Government Organization (NGO) lawsuit settlement pushing the Facility to comply with the IGP Level 2 corrective action. The Facility’s primary tasks include receiving waste materials from private citizens and local garbage collection companies; food waste recycling; metal, paper and plastic recyclables collection; and trucking of the collected materials offsite. As the Facility contains multiple industrial processes, contributing to contaminants of many kinds, the next step was to determine a treatment BMP that would manage all stormwater streams, while also meeting compliance under the IGP. This presentation will detail a (5) step approach for treatment BMP selection: water characterization, treatability testing, bench-scale testing, pilot testing and final system design. It is through this ‘best-fit’ process that the Facility was able to collect treatment system performance data, prove system efficacy, evaluate long-term risk and remain economically feasible.