Dry Weather Sanitary Sewer Overflows

Dry Weather Sanitary

This study investigates arrivals of sanitary sewer overflows collected from a Dry Weather Sanitary municipality. The data set consists of recorded overflows from 2011 to 2014 during Dry Weather Sanitary . Reliability analysis is conducted upon each data set. The Weibull distribution is adopted to evaluate the data sets. The results show that the arrival of dry weather SSOs cannot be simply modeled with a Poisson process that is featured with a constant arrival rate. For annual data set, 2-parameter Weibull generally has an acceptable fitting (except 2014 data). The shape parameters are close to 1 or a little greater than 1, indicating relatively constant arrival rate or slightly increased rate. For the entire data set, the 3-parameter Weibull distribution is able to fit the data well. The shape parameter is also greater than 1. Therefore, an increased SSO arrival rate is noticed for this data set. There are needs to make more efforts in maintaining the sewer system.

The sanitary sewer overflow (SSO) is a condition in which untreated sewage is discharged from a sanitary sewer into the surroundings before reaching to the treatment facility. The cause of SSOs can be of multiple sources, such as blockage of sewer pipelines, infiltration of storm water into the line during rainfall, pump station failures, and broken or collapsed pipe lines. Therefore, in the sewer pipeline management, the sanitary sewer overflow (SSO) is an important indicator of the system’s performance. Such SSOs are common issues to all municipalities. It is meaningful to study such events to assist decision makers in the facility management.

The arrivals of those events may be assumed to random. Such assumption of independent successive events leads to the Poisson process. In a Poisson process, the inter-arrival times display an exponential distribution [1] . Such memory-less property provides advantages in modeling events in sequence. In civil engineering, this approach has been applied in modeling rainfalls [2] , and accident frequency analysis [3] .

In real life, the homogenous memory-less assumption may not be justified. The prediction may also be conservative [4] . The process can be more accurately modeled with a non-homogenous model. Existing reliability models include Crow’s model [5] and Cox-lewis’ mode [6] , both use the Weibull distribution to fit the “time to failure” data. In this study, in order to explore the arrival of overflows during the dry weather, the Weibull distribution is used to verify the pattern of SSO arrivals. The results will assist stakeholder and decision makers in the management of sewer facilities [7] .

In the sewer system management, research has been conducted in several directions. In general, it can be categorized into three major concentrations. The first concentration is the reliability analysis of the system. This method focuses on the failure data and explores the reliability through the data analysis. Atypical example is the research conducted by Jin and Mukherjee [7] [8] . In this research, the authors focused on the blockages, by using a set of data collected on a sewer system. They proposed methods to explore the arrival patterns deeply. They also proposed a life time trend based on the reliability analysis. Applications are also explored with specific examples demonstrated. Similar research has also been conducted in terms of hydraulic evaluation of systems [9] – [12] ; and situational simulation to support decision making in co-dependent infrastructure systems [13] .

The second category is the condition prediction. The Markov chains model has been applied extensively in this research concentration. For example, Wirahadikusumah et al. discussed several challenging issues in the sewer pipe condition prediction [13] . They presented the Markov chains model, which is the most popular technique in simulating the condition deterioration. However, the condition transition probability estimation is a huge challenge. Jin and Mukherjee further proposed specific methods to estimate the probabilities [14] [15] . They further investigated the sensitivity of Markov chains model, which is a big step in this research category. Other related studies can be found in [16] – [19] .

The third category can be the life cycle analysis of the sewer system. Both life cycle cost and life cycle assessment have been conducted upon the system. For example, Najafi and Kim compared the life cycle cost of the trenchless and conventional open-cut pipeline [20] . They conclude that the trenchless method has its advantages. Lassaux et al. conducted a life cycle assessment of the water from the pump station to the wastewater treatment plant [21] . Detailed inventories are summarized in the study. Jin conducted both the life cycle cost and life cycle assessment of three rigid sewer pipes, namely, non-reinforced concrete pipe (NRCP), reinforced concrete pipe (RCP) and Vitrified clay pipe (VCP) [22] . Detailed costs and environmental impacts are presented in the study. Other related research can be found in [23] – [25] .

This research is similar to the study conducted by Jin and Mukherjee [7] . Instead of focusing on the sewer blockage, this study evaluates the SSO issues under the dry weather condition. In order to investigate the reliability of the sewer system via SSO data, the Weibull distribution is applied upon various sets of SSO data combinations.