Biodegradation of Organophosphate Chemical Warfare Agents by Activated Sludge

Organophosphates (OPs) have been widely used as Chemical Warfare Agents (CWAs) as well as pesticides since World War II and still remain a threat to national security. While efforts have been taken at military installations and civilian communities to secure these chemicals and prevent their misuse, a determined adversary could still obtain and deploy them to injure, kill, or instill terror. The lethal properties of this group of compounds are primarily owed to their irreversible inhibition of the enzyme acetyl cholinesterase (AChE) and thus may alter the human nervous system or affect the hormonal balance of children in particular. In the event of a chemical incident, standard operating procedures dictate that contaminated personnel be decontaminated. Often times, decontamination is accomplished with water. Many communities plan for this decontamination water to be sent to the local municipal wastewater treatment plant. However, the fate of these compounds in a municipal wastewater treatment plant is largely unknown. If the compounds cannot be degraded, they will enter surface water bodies with plant effluent or waste sludge. This study investigated the fate of ethyl methylphosphonic acid (EMPA), a hydrolysis product of VX, in a single sludge laboratory-scale sequencing batch reactor (SBR) that simulated a municipal activated sludge wastewater treatment plant. The reactor was fed peptone and sodium acetate to simulate wastewater. Sorption kinetics, sorption equilibrium isotherm, and degradation batch experiments demonstrated that EMPA did not sorb to the biomass. Degradation results showed that approximately 28% of the initial concentration of 1 mg L-1 EMPA was degraded. In addition, the results suggest that the nitrifying bacteria may be responsible for the degradation via cometabolism. Therefore, CWA may pass through an activated sludge wastewater treatment plant completely unchanged

A Sustainable Prototype for Renewable Energy: Optimized Prime-power Generator Solar Array Replacement

Remote locations such as disaster relief camps, isolated arctic communities, and military forward operating bases are disconnected from traditional power grids forcing them to rely on diesel generators with a total installed capacity of 10,000 MW worldwide. The generators require a constant resupply of fuel, resulting in increased operating costs, negative environmental impacts, and challenging fuel logistics. To enhance remote site sustainability, planners can develop stand-alone photovoltaic-battery systems to replace existing prime power generators. This paper presents the development of a novel cost-performance model capable of optimizing solar array and Li-ion battery storage size by generating tradeoffs between minimizing initial system cost and maximizing power reliability. A case study for the replacement of an 800 kW generator, the US Air Force’s standard for prime power at deployed locations, was analyzed to demonstrate the model and its capabilities. A MATLAB model, simulating one year of solar data, was used to generate an optimized solution to minimize initial cost while providing over 99% reliability. Replacing a single diesel generator would result in a savings of 1.9 million liters of fuel, eliminating 100 fuel tanker truck deliveries annually. The distinctive capabilities of this model enable designers to enhance environmental, economic, and operational sustainability of remote locations by creating energy self-sufficient sites, which can operate indefinitely without the need for resupply

The Viability and Simplicity of 3D-Printed Construction: A Military Case Study

In November 2019, U.S. Marines, Air Force, and Army Corps of Engineers personnel demonstrated the viability and simplicity of three-dimensionally (3D)-printed construction in a controlled environment at the U.S. Army Engineer Research and Development Center—Construction Engineering Research Laboratory in Champaign, Illinois. The tri-service exercise spanned three days and culminated in the construction of three 1 m × 1 m × 1 m (3 ft × 3 ft × 3 ft) concrete dragon’s teeth (square pyramid military fortifications used to defend against tanks and armored vehicles) and several custom-designed objects. The structural components were printed using a custom-built, gantry-style printer called ACES Lite 2 and a commercially available, proprietary mortar mix. This paper examines the viability of using 3D-printed construction in remote, isolated, and expeditionary environments by considering the benefits and challenges associated with the printing materials, structural design, process efficiency, labor demands, logistical considerations, environmental impact, and project cost. Based on the results of this exercise, 3D-printed construction was found to be faster, safer, less labor-intensive, and more structurally efficient than conventional construction methods: the dragon’s teeth were printed in an average of 57 min each and required only two laborers. However, the use of commercially procured, pre-mixed materials introduced additional cost, logistical burden, and adverse environmental impact as compared to traditional, on-site concrete mixing and production. Finally, this paper suggests future applications and areas of further research for 3D-printed construction

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

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

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

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

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

Machine Learning Modeling of Horizontal Photovoltaics Using Weather and Location Data

Solar energy is a key renewable energy source; however, its intermittent nature and potential for use in distributed systems make power prediction an important aspect of grid integration. This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations. We performed our data collection and analysis in the absence of irradiation data—an approach not commonly found in prior literature. Using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud ceiling as independent variables, a distributed random forest regression algorithm modeled the combined dataset with an R2 value of 0.94. As a comparative measure, other machine learning algorithms resulted in R2 values of 0.50–0.94. Additionally, the data from each location was modeled separately with R2 values ranging from 0.91 to 0.97, indicating a range of consistency across all sites. Using an input variable permutation approach with the random forest algorithm, we found that the three most important variables for power prediction were ambient temperature, humidity, and cloud ceiling. The analysis showed that machine learning potentially allowed for accurate power prediction while avoiding the challenges associated with modeled irradiation data

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