Evaluation of Municipal Wastewater Treatment Plant Activated Sludge for Biodegradation of Propylene Glycol as an Aircraft Deicer

Aircraft deicing fluid used at airport facilities is often collected for treatment or disposal in order to prevent serious ecological threats to nearby surface waters. This study investigated lab scale degradation of propylene glycol (PG), the active ingredient in a common aircraft deicing fluid, by way of a laboratory-scale sequencing batch reactor containing municipal waste water treatment facility activated sludge (AS) performing simultaneous organic carbon oxidation and nitrification. The ability of AS to remove PG was evaluated by studying the biodegradation and sorption characteristics of PG in an AS medium. The results indicate sorption may play a role in the fate of PG in AS, and the heterotrophic bacteria readily degrade this compound. Therefore, a field deployable SBR may be appropriate for use in flight line applications

Cost Analysis of Optimized Islanded Energy Systems in a Dispersed Air Base Conflict

The United States Air Force has implemented a dispersed air base strategy to enhance mission effectiveness for near-peer conflicts. Asset dispersal places many smaller bases across a wide geographic area, which increases resupply requirements and logistical complexity. Hybrid energy systems reduce resupply requirements through sustainable, off-grid energy production. This paper presents a novel hybrid energy renewable delivery system (HERDS) model capable of (1) selecting the optimal hybrid energy system design that meets demand at the lowest net present cost and (2) optimizing the delivery of the selected system using existing Air Force cargo aircraft. The novelty of the model’s capabilities is displayed using Clark Air Base, Philippines as a case study. The HERDS model selected an optimal configuration consisting of a 676-kW photovoltaic array, an 1846-kWh battery system, and a 200-kW generator. This hybrid energy system predicts a 54% reduction in cost and an 88% reduction in fuel usage, as compared to the baseline Air Force system. The HERDS model is expected to support planners in their ongoing efforts to construct cost-effective sites that minimize the transport and logistic requirements associated with remote installations. Additionally, the results of this paper may be appropriate for broader civilian applications

A Simulation–optimization Framework for Post-disaster Allocation of Mental Health Resources

Extreme events, such as natural or human-caused disasters, cause mental health stress in affected communities. While the severity of these outcomes varies based on socioeconomic standing, age group, and degree of exposure, disaster planners can mitigate potential stress-induced mental health outcomes by assessing the capacity and scalability of early, intermediate, and long-term treatment interventions by social workers and psychologists. However, local and state authorities are typically underfunded, understaffed, and have ongoing health and social service obligations that constrain mitigation and response activities. In this research, a resource assignment framework is developed as a coupled-state transition and linear optimization model that assists planners in optimally allocating constrained resources and satisfying mental health recovery priorities post-disaster. The resource assignment framework integrates the impact of a simulated disaster on mental health, mental health provider capacities, and the Center for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI) to identify vulnerable populations needing additional assistance post-disaster. In this study, we optimally distribute mental health clinicians to treat the affected population based upon rule sets that simulate decision-maker priorities, such as economic and social vulnerability criteria. Finally, the resource assignment framework maps the mental health recovery of the disaster-affected populations over time, providing agencies a means to prepare for and respond to future disasters given existing resource constraints. These capabilities hold the potential to support decision-makers in minimizing long-term mental health impacts of disasters on communities through improved preparation and response activities

Optimizing the Environmental and Economic Sustainability of Remote Community Infrastructure

Remote communities such as rural villages, post-disaster housing camps, and military forward operating bases are often located in remote and hostile areas with limited or no access to established infrastructure grids. Operating these communities with conventional assets requires constant resupply, which yields a significant logistical burden, creates negative environmental impacts, and increases costs. For example, a 2000-member isolated village in northern Canada relying on diesel generators required 8.6 million USD of fuel per year and emitted 8500 tons of carbon dioxide. Remote community planners can mitigate these negative impacts by selecting sustainable technologies that minimize resource consumption and emissions. However, the alternatives often come at a higher procurement cost and mobilization requirement. To assist planners with this challenging task, this paper presents the development of a novel infrastructure sustainability assessment model capable of generating optimal tradeoffs between minimizing environmental impacts and minimizing life-cycle costs over the community’s anticipated lifespan. Model performance was evaluated using a case study of a hypothetical 500-person remote military base with 864 feasible infrastructure portfolios and 48 procedural portfolios. The case study results demonstrated the model’s novel capability to assist planners in identifying optimal combinations of infrastructure alternatives that minimize negative sustainability impacts, leading to remote communities that are more self-sufficient with reduced emissions and costs

Improving Data-Driven Infrastructure Degradation Forecast Skill with Stepwise Asset Condition Prediction Models

Organizations with large facility and infrastructure portfolios have used asset management databases for over ten years to collect and standardize asset condition data. Decision makers use these data to predict asset degradation and expected service life, enabling prioritized maintenance, repair, and renovation actions that reduce asset life-cycle costs and achieve organizational objectives. However, these asset condition forecasts are calculated using standardized, self-correcting distribution models that rely on poorly-fit, continuous functions. This research presents four stepwise asset condition forecast models that utilize historical asset inspection data to improve prediction accuracy: (1) Slope, (2) Weighted Slope, (3) Condition-Intelligent Weighted Slope, and (4) Nearest Neighbor. Model performance was evaluated against BUILDER SMS, the industry-standard asset management database, using data for five roof types on 8549 facilities across 61 U.S. military bases within the United States. The stepwise Weighted Slope model more accurately predicted asset degradation 92% of the time, as compared to the industry standard’s continuous self-correcting prediction model. These results suggest that using historical condition data, alongside or in-place of manufacturer expected service life, may increase the accuracy of degradation and failure prediction models. Additionally, as data quantity increases over time, the models presented are expected to improve prediction skills. The resulting improvements in forecasting enable decision makers to manage facility assets more proactively and achieve better returns on facility investments. © 2022 by the authors

Meeting Temporary Facility Energy Demand with Climate-Optimized Off-Grid Energy Systems

Remote and contingency operations, including military and disaster-relief activities, often require the use of temporary facilities powered by inefficient diesel generators that are expensive to operate and maintain. Site planners can reduce operating costs by increasing shelter insulation and augmenting generators with photovoltaic-battery hybrid energy systems, but they must select the optimal design configuration based on the region’s climate to meet the power demand at the lowest cost. To assist planners, this paper proposes an innovative, climate-optimized, hybrid energy system selection model capable of selecting the facility insulation type, solar array size, and battery backup system to minimize the annual operating cost. To demonstrate the model’s capability in various climates, model performance was evaluated for applications in southwest Asia and the Caribbean. For a facility in Southwest Asia, the model reduced fuel consumption by 93% and saved $271 thousand compared to operating a diesel generator. The simulated facility in the Caribbean resulted in more significant savings, decreasing fuel consumption by 92$291 thousand. This capability is expected to support planners of remote sites in their ongoing effort to minimize fuel supply requirements and annual operating costs of temporary facilities

Weather-related Construction Delays in a Changing Climate: A Systematic State-of-the-art Review

Adverse weather delays forty-five percent of construction projects worldwide, costing project owners and contractors billions of dollars in additional expenses and lost revenue each year. Additionally, changes in climate are expected to increase the frequency and intensity of weather conditions that cause these construction delays. Researchers have investigated the effect of weather on several aspects of construction. Still, no previous study comprehensively (1) identifies and quantifies the risks weather imposes on construction projects, (2) categorizes modeling and simulation approaches developed, and (3) summarizes mitigation strategies and adaptation techniques to provide best management practices for the construction industry. This paper accomplishes these goals through a systematic state-of-the-art review of 3207 articles published between 1972 and October 2020. This review identified extreme temperatures, precipitation, and high winds as the most impactful weather conditions on construction. Despite the prevalence of climate-focused delay studies, existing research fails to account for future climate in the modeling and identification of delay mitigation strategies. Accordingly, planners and project managers can use this research to identify weather-vulnerable activities, account for changing climate in projects, and build administrative or organizational capacity to assist in mitigating weather delays in construction. The cumulative contribution of this review will enable sustainable construction scheduling that is robust to a changing climate

United States Department of Defense (DoD) Real Property Repair, Alterations, Maintenance, and Construction Project Contract Data: 2009–2020

Nearly one-half of all construction projects exceed planned costs and schedule, globally [1]. Owners and construction managers can analyze historical project performance data to inform cost and schedule overrun risk-reduction strategies. Though, the majority of open-source project datasets are limited by the number of projects, data dimensionality, and location. A significant global customer of the construction industry, the Department of Defense (DoD) maintains a vast database of historical project data that can be used to determine the sources and magnitude of construction schedule and cost overruns for many continental and international locations. The selection of data provided by the authors is a subset of the U.S. Federal Procurement Data System-Next Generation (FPDS-NG), which stores contractual obligations made by the U.S. Federal Government [2]. The data comprises more than ten fiscal years (1 Oct 2009 – 04 June 2020) of construction contract attributes that will enable researchers to investigate spatiotemporal schedule and cost performance by, but not limited to: contract type, construction type, delivery method, award date, and award value. To the knowledge of the authors, this is the most extensive open-source dataset of its kind, as it provides access to the contract data of 132,662 uniquely identified construction projects totaling $865 billion. Because the DoD\u27s facilities and infrastructure construction requirements and use of private construction firms are congruent with the remainder of the public sector and the private sector, results obtained from analyses of this dataset may be appropriate for broader application

A Framework for Estimating the United States Depression Burden Attributable to Indoor Fine Particulate Matter Exposure

Recently published exploratory studies based on exposure to outdoor fine particulates, defined as particles with a nominal mean diameter less than or equal to 2.5 μm (PM2.5) indicate that the pollutant may play a role in mental health conditions, such as major depressive disorder. This paper details a model that can estimate the United States (US) major depressive disorder burden attributable to indoor PM2.5 exposure, locally modifiable through input parameter calibrations. By utilizing concentration values in an exposure-response function, along with relative risk values derived from epidemiological studies, the model estimated the prevalence of expected cases of major depressive disorder in multiple scenarios. Model results show that exposure to indoor PM2.5 might contribute to 476,000 cases of major depressive disorder in the US (95% confidence interval 11,000–1,100,000), approximately 2.7% of the total number of cases reported annually. Increasing heating, ventilation, and air conditioning (HVAC) filter efficiency in a residential dwelling results in minor reductions in depressive disorders in rural or urban locations in the US. Nevertheless, a minimum efficiency reporting value (MERV) 13 filter does have a benefit/cost ratio at or near one when smoking occurs indoors; during wildfires; or in locations with elevated outdoor PM2.5 concentrations. The approach undertaken herein could provide a transparent strategy for investment into the built environment to improve the mental health of the occupants

Prioritizing Facilities Linked to Corporate Strategic Objectives Using a Fuzzy Model

Excerpt: Limited facilities operating and modernization budgets require organizations to carefully identify, prioritize and authorize projects to ensure allocated resources align with strategic objectives. Traditional facility prioritization methods using risk matrices can be improved to increase granularity in categorization and avoid mathematical error or human cognitive biases. These limitations restrict the utility of prioritizations and if erroneously used to select projects for funding, they can lead to wasted resources. This paper aims to propose a novel facility prioritization methodology that corrects these assessment design and implementation issues