Some Factors Affecting Inflow and Infiltration from Residential Sources in a Core Urban Area: Case Study in a Columbus, Ohio, Neighborhood

Abstract

Stormwater infiltration and inflow are major contributors to sewer flow, and thus they can be significant triggers for combined and sanitary sewer overflows, both of which introduce contaminants to surface waters. There are few estimates of private residential rainfall-dependent inflow and infiltration (RDII), and this paper proposes that a rapid and cost-effective means to locate points of significant stormwater entry into the sewer system would be advantageous for combined and sanitary sewer management. The authors studied the collection system in the Barthman-Parsons area of Columbus, Ohio, performing detailed drainage and connectivity investigations on 116 private houses located in areas served by separated sanitary sewers. Sources of inflow and infiltration (I/I) were identified for private residential properties, which could include sump pumps, foundation drains, downspouts, cleanouts, yard drains, and defective service laterals. The authors then developed estimates of I/I contributions from residential properties in different neighborhood clusters, and these estimates were extrapolated to the entire Barthman-Parsons area. Field results found that 68% of tested properties contribute to I/I. Of the tested sample, 25% had at least one downspout that tested positive for I/I, and 59% had a lateral that tested positive. The results also showed that downspouts and laterals contributed approximately 98% of the total I/I volume generated by the tested properties in response to testing. These residential I/I sources were estimated to make up approximately 35% of total I/I for short, intense storms with dry antecedent conditions, and approximately 7% of total I/I under low-intensity, long-duration storms with wet antecedent conditions. An internally validated logistic model developed for the downspout data set was fairly accurate at predicting whether a property would test positive. It correctly classifies the downspouts of 17 of 27 houses as contributing I/I, and incorrectly predicts 14 as contributing. Had this model been available prior to testing, a majority of the houses’ downspouts contributing to I/I could be eliminated without testing the entire population, offering significant savings in assessment and testing costs, and leading to an overall faster turnaround in system improvement.