Urban Water Services in Fragile States: An Analysis of Drinking Water Sources and Quality in Port Harcourt, Nigeria, and Monrovia, Liberia.

Establishing and maintaining public water services in fragile states is a significant development challenge. In anticipation of water infrastructure investments, this study compares drinking water sources and quality between Port Harcourt, Nigeria, and Monrovia, Liberia, two cities recovering from political and economic instability. In both cities, access to piped water is low, and residents rely on a range of other private and public water sources. In Port Harcourt, geographic points for sampling were randomly selected and stratified by population density, whereas in Monrovia, locations for sampling were selected from a current inventory of public water sources. In Port Harcourt, the sampling frame demonstrated extensive reliance on private boreholes and a preference, in both planned and unplanned settlements, for drinking bottled and sachet water. In Monrovia, sample collection focused on public sources (predominantly shallow dug wells). In Port Harcourt, fecal indicator bacteria (FIB) were detected in 25% of sources (N = 566), though concentrations were low. In Monrovia, 57% of sources contained FIB and 22% of sources had nitrate levels that exceeded standards (N = 204). In Monrovia, the convenience of piped water may promote acceptance of the associated water tariffs. However, in Port Harcourt, the high prevalence of self-supply and bottled and sachet drinking water suggests that the consumer's willingness to pay for ongoing municipal water supply improvements may be determined by service reliability and perceptions of water quality.

A neural network model for predicting aquifer water level elevations.

Artificial neural networks (ANNs) were developed for accurately predicting potentiometric surface elevations (monitoring well water level elevations) in a semiconfined glacial sand and gravel aquifer under variable state, pumping extraction, and climate conditions. ANNs "learn" the system behavior of interest by processing representative data patterns through a mathematical structure analogous to the human brain. In this study, the ANNs used the initial water level measurements, production well extractions, and climate conditions to predict the final water level elevations 30 d into the future at two monitoring wells. A sensitivity analysis was conducted with the ANNs that quantified the importance of the various input predictor variables on final water level elevations. Unlike traditional physical-based models, ANNs do not require explicit characterization of the physical system and related physical data. Accordingly, ANN predictions were made on the basis of more easily quantifiable, measured variables, rather than physical model input parameters and conditions. This study demonstrates that ANNs can provide both excellent prediction capability and valuable sensitivity analyses, which can result in more appropriate ground water management strategies.