Post-project assessment and follow-up support for community managed rural water systems in Panama

The Environmental Health (EH) program of Peace Corps (PC) Panama and a non-governmental organization (NGO) Waterlines have been assisting rural communities in Panama gain access to improved water sources through the practice of community management (CM) model and participatory development. Unfortunately, there is little information available on how a water system is functioning once the construction is complete and the volunteer leaves the community. This is a concern when the recent literature suggests that most communities are not able to indefinitely maintain a rural water system (RWS) without some form of external assistance (Sara and Katz, 1997; Newman et al, 2002; Lockwood, 2002, 2003, 2004; IRC, 2003; Schweitzer, 2009). Recognizing this concern, the EH program director encouraged the author to complete a postproject assessment of the past EH water projects. In order to carry out the investigation, an easy to use monitoring and evaluation tool was developed based on literature review and the author’s three years of field experience in rural Panama. The study methodology consists of benchmark scoring systems to rate the following ten indicators: watershed, source capture, transmission line, storage tank, distribution system, system reliability, willingness to pay, accounting/transparency, maintenance, and active water committee members. The assessment of 28 communities across the country revealed that the current state of physical infrastructure, as well as the financial, managerial and technical capabilities of water committees varied significantly depending on the community. While some communities are enjoying continued service and their water committee completing all of its responsibilities, others have seen their water systems fall apart and be abandoned. Overall, the higher score were more prevalent for all ten indicators. However, even the communities with the highest scores requested some form of additional assistance. The conclusion from the assessment suggests that the EH program should incorporate an institutional support mechanism (ISM) to its sector policy in order to systematically provide follow-up support to rural communities in Panama. A full-time circuit rider with flexible funding would be able to provide additional technical support, training and encouragement to those communities in need.

Evaluation of surface defect detection in reinforced concrete bridge decks using terrestrial LiDAR

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.

Analysis of spring break-up and its effects on a biomass feedstock supply chain in northern Michigan

Demand for bio-fuels is expected to increase, due to rising prices of fossil fuels and concerns over greenhouse gas emissions and energy security. The overall cost of biomass energy generation is primarily related to biomass harvesting activity, transportation, and storage. With a commercial-scale cellulosic ethanol processing facility in Kinross Township of Chippewa County, Michigan about to be built, models including a simulation model and an optimization model have been developed to provide decision support for the facility. Both models track cost, emissions and energy consumption. While the optimization model provides guidance for a long-term strategic plan, the simulation model aims to present detailed output for specified operational scenarios over an annual period. Most importantly, the simulation model considers the uncertainty of spring break-up timing, i.e., seasonal road restrictions. Spring break-up timing is important because it will impact the feasibility of harvesting activity and the time duration of transportation restrictions, which significantly changes the availability of feedstock for the processing facility. This thesis focuses on the statistical model of spring break-up used in the simulation model. Spring break-up timing depends on various factors, including temperature, road conditions and soil type, as well as individual decision making processes at the county level. The spring break-up model, based on the historical spring break-up data from 27 counties over the period of 2002-2010, starts by specifying the probability distribution of a particular county’s spring break-up start day and end day, and then relates the spring break-up timing of the other counties in the harvesting zone to the first county. In order to estimate the dependence relationship between counties, regression analyses, including standard linear regression and reduced major axis regression, are conducted. Using realizations (scenarios) of spring break-up generated by the statistical spring breakup model, the simulation model is able to probabilistically evaluate different harvesting and transportation plans to help the bio-fuel facility select the most effective strategy. For early spring break-up, which usually indicates a longer than average break-up period, more log storage is required, total cost increases, and the probability of plant closure increases. The risk of plant closure may be partially offset through increased use of rail transportation, which is not subject to spring break-up restrictions. However, rail availability and rail yard storage may then become limiting factors in the supply chain. Rail use will impact total cost, energy consumption, system-wide CO2 emissions, and the reliability of providing feedstock to the bio-fuel processing facility.