There are limited expressions capable of estimating removals in one of the world's oldest and most sustainable water treatment systems: slow-rate biofilters. This research addresses the problem by deriving semi-empirical models that predict pathogen and natural organic matter removals within these natural and engineered sand filters. The more complex pathogen model, or phenomenological colloidal filtration theory (pCFT), applies the 1937 Iwasaki solution to New England pilot scale E. coli observations. The derived pCFT was then calibrated through a series of experimental bench scale phases. Further pCFT validation came by way of a seamless application to multiple microorganisms. Viruses (MS2 as surrogate) and aerobic spore-forming bacteria (ASFB) appear to be less subjected to phenomenological filtration than that of Enterococci, E. coli and total coliforms. While variables remain unknown, the aforementioned microbiological contamination indicators can be modeled to within one log removal for a given source water and filter. Modeling natural organic matter, on the other hand, is primarily based on biological processes in SRBFs following first order kinetics as a function of filter residence time. This makes the above natural organic matter model directly proportional to the amount of biologically degradable dissolved organic carbon available in a given source water and filter. While multiple questions remain within the derived expressions, the resulting math provides a new level of efficacy in modeling removal capabilities for slow-rate biofilters.
","abstract_html":"<p>There are limited expressions capable of estimating removals in one of the world's oldest and most sustainable water treatment systems: slow-rate biofilters. This research addresses the problem by deriving semi-empirical models that predict pathogen and natural organic matter removals within these natural and engineered sand filters. The more complex pathogen model, or phenomenological colloidal filtration theory (pCFT), applies the 1937 Iwasaki solution to New England pilot scale E. coli observations. The derived pCFT was then calibrated through a series of experimental bench scale phases. Further pCFT validation came by way of a seamless application to multiple microorganisms. Viruses (MS2 as surrogate) and aerobic spore-forming bacteria (ASFB) appear to be less subjected to phenomenological filtration than that of Enterococci, E. coli and total coliforms. While variables remain unknown, the aforementioned microbiological contamination indicators can be modeled to within one log removal for a given source water and filter. Modeling natural organic matter, on the other hand, is primarily based on biological processes in SRBFs following first order kinetics as a function of filter residence time. This makes the above natural organic matter model directly proportional to the amount of biologically degradable dissolved organic carbon available in a given source water and filter. While multiple questions remain within the derived expressions, the resulting math provides a new level of efficacy in modeling removal capabilities for slow-rate biofilters.</p>","abstract_has_math":false,"creators":["Senders, Jeffrey Dale"],"institution":null,"degree_name":"Doctor of Philosophy","degree_level":"Dissertation","degree_discipline":null,"degree_department":null,"school":null,"contributors":["M R Collins"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-01-01T08:00:00Z","date_published":"2013-01-01T08:00:00Z","updated_at":"2026-07-24T05:22:42Z","subjects":["Engineering","Civil","Environmental","Environmental Sciences","Civil Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholars.unh.edu/dissertation/721","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["M R Collins"]},{"key":"dc:creator","label":"Author","values":["Senders, Jeffrey Dale"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering","Civil","Environmental","Environmental Sciences","Civil Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholars.unh.edu/dissertation/721"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["There are limited expressions capable of estimating removals in one of the world's oldest and most sustainable water treatment systems: slow-rate biofilters. This research addresses the problem by deriving semi-empirical models that predict pathogen and natural organic matter removals within these natural and engineered sand filters. The more complex pathogen model, or phenomenological colloidal filtration theory (pCFT), applies the 1937 Iwasaki solution to New England pilot scale E. coli observations. The derived pCFT was then calibrated through a series of experimental bench scale phases. Further pCFT validation came by way of a seamless application to multiple microorganisms. Viruses (MS2 as surrogate) and aerobic spore-forming bacteria (ASFB) appear to be less subjected to phenomenological filtration than that of Enterococci, E. coli and total coliforms. While variables remain unknown, the aforementioned microbiological contamination indicators can be modeled to within one log removal for a given source water and filter. Modeling natural organic matter, on the other hand, is primarily based on biological processes in SRBFs following first order kinetics as a function of filter residence time. This makes the above natural organic matter model directly proportional to the amount of biologically degradable dissolved organic carbon available in a given source water and filter. While multiple questions remain within the derived expressions, the resulting math provides a new level of efficacy in modeling removal capabilities for slow-rate biofilters.
"]},{"key":"dc:title","label":"Title","values":["Approaches to modeling pathogen and natural organic matter removals in slow-rate biofilters"]}]}],"canonical_facts":{"dc:contributor":["M R Collins"],"dc:creator":["Senders, Jeffrey Dale"],"dc:description.abstract":["There are limited expressions capable of estimating removals in one of the world's oldest and most sustainable water treatment systems: slow-rate biofilters. This research addresses the problem by deriving semi-empirical models that predict pathogen and natural organic matter removals within these natural and engineered sand filters. The more complex pathogen model, or phenomenological colloidal filtration theory (pCFT), applies the 1937 Iwasaki solution to New England pilot scale E. coli observations. The derived pCFT was then calibrated through a series of experimental bench scale phases. Further pCFT validation came by way of a seamless application to multiple microorganisms. Viruses (MS2 as surrogate) and aerobic spore-forming bacteria (ASFB) appear to be less subjected to phenomenological filtration than that of Enterococci, E. coli and total coliforms. While variables remain unknown, the aforementioned microbiological contamination indicators can be modeled to within one log removal for a given source water and filter. Modeling natural organic matter, on the other hand, is primarily based on biological processes in SRBFs following first order kinetics as a function of filter residence time. This makes the above natural organic matter model directly proportional to the amount of biologically degradable dissolved organic carbon available in a given source water and filter. While multiple questions remain within the derived expressions, the resulting math provides a new level of efficacy in modeling removal capabilities for slow-rate biofilters.
"],"dc:identifier":["https://scholars.unh.edu/dissertation/721"],"dc:subject":["Engineering","Civil","Environmental","Environmental Sciences","Civil Engineering"],"dc:title":["Approaches to modeling pathogen and natural organic matter removals in slow-rate biofilters"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy"]},"updated_at":"2026-07-24T05:22:42Z"}