Utility District Research Articles

Organizational fragmentation and service performance of municipal water districts

ABSTRACT Existing research identifies numerous organizational and contextual covariates that influence how public sector service organizations perform. In the context of drinking water provision, functional fragmentation (via specialized utility districts) and the resulting competition is often touted as a means to achieving better service results. However, fragmentation can potentially result in excessive density or disproportionate resource partitioning between water utilities, perhaps even resulting in serial underperformance. Evidence from municipal water districts shows that, while the argument for competition-service quality link appears to be valid, the concentration-service quality link and the incidences of performance path-dependencies should be of significant concern to local governments.

Cost efficiency of Tunisian water utility districts: Does heterogeneity matter?

We examine the cost inefficiencies in the Tunisian water industry by controlling the heterogeneity bias. Two different cost-stochastic frontier models are tested to determine how observed and unobserved heterogeneity affect water utility districts' performance rankings and efficiency scores. The correction for heterogeneity significantly affected rankings by efficiency measures, and the two models yielded different results. The true fixed-effects model is more reliable than the pooled stochastic model. Then district's utility set still exhibits latent heterogeneity and significant cost inefficiencies are observed. The model estimates demonstrate the existence of economies of scale and density with respect to the size of the operation. Small district utilities exhibit economies of scale, while large district utilities exhibit diseconomies of scale.

Biofouling of filtration membranes in wastewater reuse: In situ visualization with confocal laser scanning microscopy

The filtration membranes utilized in advanced treatment of municipal wastewater are prone to biological fouling. Permeability loss and water recovery limitations due to fouling necessitate additional pretreatment and membrane cleaning, which raise the cost of water reuse. Better fouling mitigation calls for a deeper understanding of biofilm behavior on water reuse membranes. This study uses confocal microscopy to monitor biofouling under realistic conditions. To simulate water reuse, a miniature flow cell with microfiltration and ultrafiltration membranes was operated under typical filtration conditions using secondary effluent from California’s East Bay Municipal Utility District. The growing biofilms were stained for extracellular polymeric substances and live and dead cells, and they were monitored in situ under filtration conditions with a confocal laser scanning microscope to produce 2-D and 3-D images. In situ image series revealed biofilm growth and removal processes including internal and external fouling, extracellular polymeric substance production, wrinkling, delamination, and film re-deposition. Additionally, changes in biofilm morphology between in situ and ex situ images highlighted limitations of ex situ imaging. These results provide insight into the physical and biological mechanisms of biofouling in hopes of informing the development of improved techniques for biofouling mitigation.

Identifying causal changes in landscape greenness with very high-resolution airborne multispectral imagery and a panel data model

To effectively manage urban environmental systems, interventions on private lands may sometimes be necessary. This is illustrated in many water districts where water agencies run programs that provide incentives to landholders to change their landscaping to meet the water agency’s management goals. It is difficult to monitor, track, and evaluate landscape change programs on private land, however. To most efficiently do so, very high-resolution, time-series measurements of urban landcover would be useful, but such data is often unavailable. This study evaluates how private urban landscape change is represented in the highest resolution, publicly available, multispectral data product distributed in the United States. The study focuses on residential parcels that participated in a lawn replacement rebate program run by the East Bay Municipal Utility District, a water agency in California. Each parcel has a known quantity of turfgrass converted to drought-tolerant landscaping. The study asks if Normalized Differential Vegetation Index (NDVI) values derived from National Agriculture Imagery Program (NAIP) data can be used to detect participation in a lawn replacement program at the parcel-level. While the NAIP data product has substantial error in a time-series and urban context, this study finds that approaching change detection using a fixed effect panel data model accommodates error, making it possible to identify NDVI change. With control groups defined by matching and weighing with Covariate Balancing Propensity Scores, results indicate that observed browning at the parcel-level can be causally attributed to participating in the lawn replacement program. This ability to detect small, causal changes in a heterogenous environment opens possibilities for monitoring and evaluating programs acting on private urban landscapes, contributing to more effective and efficient design, pricing and implementation.

A Simplified Method for Rapid Estimation of Emergency Water Supply Needs after Earthquakes

Researchers are investigating the problem of estimating households with potable water service outages soon after an earthquake. Most of these modeling approaches are computationally intensive, have large proprietary data collection requirements or lack precision, making them unfeasible for rapid assessment, prioritization, and allocation of emergency water resources in large, complex disasters. This study proposes a new simplified analytical method—performed without proprietary water pipeline data—to estimate water supply needs after earthquakes, and a case study of its application in the HayWired earthquake scenario. In the HayWired scenario—a moment magnitude (Mw) 7.0 Hayward Fault earthquake in the San Francisco Bay Area, California (USA)—an analysis of potable water supply in two water utility districts was performed using the University of Colorado Water Network (CUWNet) model. In the case study, application of the simplified method extends these estimates of household water service outage to the nine counties adjacent to the San Francisco Bay, aggregated by a ~250 m2 (nine-arcsecond) grid. The study estimates about 1.38 million households (3.7 million residents) out of 7.6 million residents (2017, ambient, nighttime population) with potable water service outage soon after the earthquake—about an 8% increase from the HayWired scenario estimates.

Texas Utility Pilots Water Meters That Listen for Unseen Leaks

As a way to proactively reduce nonrevenue water, Crystal Clear Special Utility District field‐tested new technology that directly integrates “listening” sensors in meters.

Toward cost-effective residential energy reduction and community impacts: A data-based machine learning approach

Many U.S. utilities incentivize residential energy reduction through rebates, often in response to state mandates for energy reduction or from a desire to reduce demand to mitigate the need to grow generating assets. The assumption built into incentive programs is that the least efficient residences will be more likely take advantage of the rebates. This, however, is not always the case. The main goal of this study was to determine the potential for prioritized incentivization, i.e., prioritizing incentives that deliver the greatest energy savings per investment through an entire community. It uses a data mining approach that leverages known building and energy characteristics for predicting energy consumption of houses that collectively can be considered representative of all residences within an entire community. From this model, it estimates natural gas consumption and savings, and corresponding implementation costs associated with the adoption of the most impactful energy reduction measures. The resulting savings and cost estimates allow us to develop a sequential energy reduction strategy whereby the most economic measures within the whole utility district are addressed. The results show that an energy reduction of 36% can be achieved at a levelized cost of less than $14 per mmBTU ($14,780 per MJ), demonstrating the strong potential of this approach. A corresponding Economic Input–Output Analysis captures the cascading community economic impacts of this strategy. The results show that for the roughly 45,000 single-family residences in the studied region, an initial energy efficiency investment of $26M could result in a total cascading multiplier economic impact of $41M and additional economic impacts of $2.2M for the lifetime of the considered energy efficiency measures.

Genome Sequencing of Sewage Detects Regionally Prevalent SARS-CoV-2 Variants.

Viral genome sequencing has guided our understanding of the spread and extent of genetic diversity of SARS-CoV-2 during the COVID-19 pandemic. SARS-CoV-2 viral genomes are usually sequenced from nasopharyngeal swabs of individual patients to track viral spread. Recently, RT-qPCR of municipal wastewater has been used to quantify the abundance of SARS-CoV-2 in several regions globally. However, metatranscriptomic sequencing of wastewater can be used to profile the viral genetic diversity across infected communities. Here, we sequenced RNA directly from sewage collected by municipal utility districts in the San Francisco Bay Area to generate complete and nearly complete SARS-CoV-2 genomes. The major consensus SARS-CoV-2 genotypes detected in the sewage were identical to clinical genomes from the region. Using a pipeline for single nucleotide variant calling in a metagenomic context, we characterized minor SARS-CoV-2 alleles in the wastewater and detected viral genotypes which were also found within clinical genomes throughout California. Observed wastewater variants were more similar to local California patient-derived genotypes than they were to those from other regions within the United States or globally. Additional variants detected in wastewater have only been identified in genomes from patients sampled outside California, indicating that wastewater sequencing can provide evidence for recent introductions of viral lineages before they are detected by local clinical sequencing. These results demonstrate that epidemiological surveillance through wastewater sequencing can aid in tracking exact viral strains in an epidemic context.

Hydroelectric H2 comes to Washington

The Douglas County Public Utility District in Washington State has selected the engineering firm Cummins to build an electrolyzer that will use excess electricity from the Wells Dam to produce hydr...

Waste to Energy Realized at Long Island Wastewater Treatment Facility

New York's Great Neck Water Pollution Control District operates a 5‐mgd wastewater treatment facility that is turning waste products into electricity and heat as well as saving money for the utility district and ratepayers.

Take an Asset Management Approach to Concrete Tank Coatings

A utility district changed its approach to concrete water storage tank maintenance by establishing an asset management program for improved tank coating and maintenance.

What is environmental racism for? Place-based harm and relational development

This paper argues that observed environmental racisms are instrumental to the development of white places. Rather than limiting the view of environmental harms to Black communities as spatial violence enacted via white NIMBYism, we argue that such spoilage is what produces white places. We call this process ‘creative extraction,’ the taking of resources from Black places to invest in white places. Those resources range from regressive sales tax revenues to water and sewer infrastructure to land devalued through waste disposal. Creative extraction involves seemingly unconnected actors reinforcing mutual interest in white spatial control and placemaking. Drawing on our case study of Montgomery County, Texas, we show how development, infrastructure, and environmental harm are intimately linked through legal and political contestation, and resource redistribution. We focus on Texas’ mechanism for creating new water infrastructure through ‘Municipal Utility Districts’ to illustrate how apolicy can create literal and symbolic boundaries to enable overdevelopment and underdevelopment. Our proposed vantage point on the relationship between communities can improve future research on both environmental racism and ‘the city’.

Biogeochemical assessment of soils and plants in industrial, residential and recreational areas of Saint Petersburg

Soils and plants of Saint Petersburg are under the constant technogenic stress caused by human activity in industrial, residential, and recreational landscapes of the city. To assess the transformed landscapes of various functional zones, we studied utility, housing, and park districts with a total area of over 7,000 hectares in the southern part of the city during the summer seasons of 2016-2018. Throughout the fieldwork period, 796 individual pairs of soil and plant samples were collected.A complex of consequent laboratory studies performed in an accredited laboratory allowed the characterization of key biogeochemical patterns of urban regolith specimens and herbage samples of various grasses. Chemical analyses provided information on the concentrations of polluting metals in soils and plants of different land use zones.Data interpretation and calculation of element accumulation factors revealed areas with the most unfavorable environmental conditions. We believe that a high pollution level in southern city districts has led to a significant degree of physical, chemical, and biological degradation of the soil and vegetation cover. As of today, approximately 10 % of the Technosols in the study area have completely lost the ability to biological self-revitalization, which results in ecosystem malfunction and the urgent need for land remediation.

Cultural Resources Survey for the Proposed MUD 121 Brazos River Levee Protection Project in Fort Bend County, Texas

This report summarizes the results of a cultural resources survey by Gray & Pape, Inc. of an approximately 14.8-hectare (36.6-acre) property in Fort Bend County, Texas, planned for a bank stabilization project on behalf of their client, Berg-Oliver Associates, Inc. The goals of the survey were to determine if the proposed project would affect any previously identified archaeological sites as defined by Section 106 of the National Historic Preservation Act of 1966, as amended (36 CFR 800), and to establish whether or not previously unidentified buried archaeological resources were located within the project’s Area of Potential Effect. Portions of the project are on property owned by Fort Bend County Municipal Utility District Number 121, political subdivisions of the state, as such, a Texas Antiquities Permit (Permit Number 8734) was required prior to the commencement of fieldwork. All fieldwork and reporting activities were completed with reference to state (the Antiquities Code of Texas) and federal guidelines. Prior to fieldwork mobilization, a background literature and site file search were conducted to identify the presence of recorded sites and previous cultural resource surveys within or near the project area. The search indicated that no previously identified archaeological sites, cemeteries, historic markers, or National Register properties are located within the project area. The same research identified that eight previous cultural resource surveys had been conducted within the study radius of the project area, one of which overlapped with the current project area. In addition, 14 previously recorded archaeological sites are located within the study radius, none of which are located within or immediately adjacent to the current project area. Field investigations were carried out in two mobilizations in January and December 2019 and consisted of a combination of pedestrian survey and subsurface testing, resulting in the excavation of 32 shovel tests. Five planned tests were left unexcavated due to inundation, and eight planned tests were left unexcavated due to significant surface disturbance. All shovel tests were negative for cultural resource material and no historic-age resources were identified during survey. After a revised scope of work was submitted to the Texas Historical Commission, investigation of deeply buried soils took place tandem with construction by regular monitoring of construction excavation. When the construction schedule allowed, traditional deep testing, by means of mechanical excavation, was carried out in five of six areas anticipated to have deep impacts from the proposed bank stabilization project. A total of 22 trenches were excavated. No buried features or deeply buried paleosols were encountered. Gray & Pape, Inc. archaeologists are of the opinion that the shovel test survey and deep testing completed within the Area of Potential Effects has adequately assessed the potential for surface and near surface intact, significant cultural resources, as well as determining the potential for deeply buried resources or paleosols. No artifacts or cultural features were encountered during the course of the survey, and no new archaeological sites were identified. No negative impacts on any previously identified sites are anticipated from the proposed project. Based on these results, Gray & Pape, Inc. recommends that no further cultural work be required and that the project be cleared to proceed as planned. As required under the provisions of Texas Antiquities Code Permit 8734, all project records are housed at the Center for Archaeological Studies at Texas State University, San Marcos, Texas.

Improved PV Forecasts for Capacity Firming

Some balancing authorities give owners of medium to large photovoltaic (PV) generation plants a choice between firming the production of their plants using battery energy storage or paying the balancing authority for the cost that these imbalances impose on the system. If the owner of a PV plant decides to do capacity firming, the net production of the PV plant and the battery must match a forecast value. A more accurate forecast of the PV production reduces the energy throughput of the battery and hence its degradation. This article compares capacity firming using persistence forecasts with predictions based on long short-term memory recurrent neural networks (LSTM-RNN), encoder-decoder LSTM-RNN and multi-layer perceptrons. This article also proposes to use the type-of-day, such as sunny, cloudy etc, which can be generated by clustering historical PV generation data according to the total daily PV generation, as a feature of the PV forecasting model. Results based on the Snohomish County Public Utility District's Arlington Microgrid show that the machine learning techniques perform significantly better than the persistence method in forecasting PV generation. In particular, encoder-decoder LSTM-RNN would reduce the yearly battery energy throughput by 29% and the number of battery cycles with a greater than 10% depth-of-discharge (DoD) by 51%. Including the day-type as a feature in PV forecasting reduces the battery energy throughput by 5.3% and the number of cycles with a DoD larger than 10% by 5.9%.

Intensive Cultural Resources Survey of the Proposed West Fork of the San Jacinto River South Drainage Channel Phase 3 Project, Montgomery County, Texas

Intensive Cultural Resources Survey of the Proposed West Fork of the San Jacinto River South Drainage Channel Phase 3 Project, Montgomery County, Texas

Choosing a designated survivor

The East Bay Municipal Utility District provides drinking water to 1.4 million customers and treats wastewater for 685,000 customers on the east side of San Francisco Bay in Northern California. The district’s products and services are vital for public health, fire protection, economic development and quality of life. The district’s award-winning business continuity programme is considered a model for the water and wastewater industry. This article discusses how the district approaches succession planning — the process of identifying and developing designated individuals who can replace critical roles in leadership or critical positions when those staff members become unavailable for any reason — with particular respect to the elected board of directors.

Spillover as a cause of bias in baseline evaluation methods for demand response programs

Prior research has shown load reduction estimates from residential event-driven demand response programs (e.g., Critical Peak Pricing) using X of the highest Y days with a weather adjustment method are the best performing within the class of currently used baseline methods. However, they are still biased relative to estimates produced from randomized control trials (RCTs), the unbiased “gold standard” evaluation method. In this paper we identify underlying factors that cause some of the bias found in one commonly used baseline method. Rather than simply quantifying bias, our research provides a deeper understanding of what causes the bias and thus can be used to develop more accurate methods that are not subject to these underlying factors. Previous studies have compared various baseline methods relative to each other; however, because all baseline methods are biased, it is impossible to determine the true bias that exists in them. We have access to a unique RCT dataset: the Sacramento Municipal Utility District’s study of critical peak pricing. Our analysis of 23 event days over two summers allows us to identify the true bias on load reduction estimates by using the RCT estimates as the unbiased gold standard against which we compare the estimates from the baseline methods. We found that spillover of energy reductions, from hours targeted by a program onto other hours, is one underlying factor that is a major cause of bias in baseline methods. We discuss alternative baseline methods that may not be subject to this same bias.

Large scale residential energy efficiency prioritization enabled by machine learning

Cost-effective energy efficiency improvements in residential buildings can realize 30% energy reduction within this energy sector for the USA. Unfortunately, audits on residences to identify potential savings, generally based on detailed energy models, tend to over-estimate anticipated savings. This leads to wariness on the part of potential investors. We address this issue by taking an integrated data- and physics-based approach, using residential building geometrical and energy system characteristics (e.g., envelope, appliances, and HVAC systems), as well as historical energy consumption for each residence and weather data, to predict monthly natural gas energy consumption and savings from the adoption of individual energy saving measures. This approach requires only those geometrical and energy system characteristics associated with the greatest potential for realized savings which are also easy to obtain. We construct a dataset from a collection of houses with a wide range of energy effectiveness, and train a single statistical model that accurately predicts natural gas energy consumption in any of the individual residences. The model is then used to estimate the savings from most impactful energy efficiency investments for individual residences, as well as the collective grouping. The specific case considered here involves hundreds of university-owned student residences in the U.S. Midwest. The resulting machine-learning derived model is used to predict monthly natural gas consumption with a mean squared error of 0.00023 for unitary scaled cross-validation data. We use a nearest neighbor approach to validate savings estimates for virtually improved residences, identifying the surrogate residence most like an improved residence. This validation demonstrates a savings prediction accuracy to within 3.5% for most of the measures considered. The validated models are shown capable of prioritizing investments among the collective of residences considered. Sequential adoption of the most cost-effective energy efficiency measure among a group of residences renders a total energy reduction of 36%. The practical implications of this research are significant. This integrated machine-learning and physics-based approach to estimate savings could be used in any utility district and throughout all of the USA to enable district-wide residential energy reduction based upon sequential adoption of the most cost-effective energy measures.

Organizational Dissolutions in the Public Sector: An Empirical Analysis of Municipal Utility Water Districts

AbstractThe proliferation of special-purpose districts and the increasing complexity of local governance systems has been well documented. However, even as new special districts are created, others are being dissolved. This article investigates the extent to which both internal and external factors are at play in municipal utility district dissolutions. Decades of existing empirical studies on private, nonprofit, and interest organizations show that factors internal to organizations, such as institutional structure and resources are significant covariates of organizational mortality. Equally important are external factors, where density dependence and resource partitioning pressures influence organizational survival. Public sector organizations, such as special-purpose water districts, operate in relatively well monitored and statutorily constrained environments, however. Drawing upon the organizational mortality literature, we examine when and why municipal utility water districts that operate in fragmented service delivery systems dissolve. The results show that the relationship between internal and external organizational variables and special-purpose organizational dissolutions is more nuanced than existing research suggests.