Leveraging water-wastewater data interdependencies to understand infrastructure systems' behaviors during COVID-19 pandemic.

Social distancing policies (SDPs) implemented worldwide in response to COVID-19 pandemic have led to spatiotemporal variations in water demand and wastewater flow, creating potential operational and service-related quality issues in water-sector infrastructure. Understanding water-demand variations is especially challenging in contexts with limited availability of smart meter infrastructure, hindering utilities' ability to respond in real time to identified system vulnerabilities. Leveraging water and wastewater infrastructures' interdependencies, this study proposes the use of high-granular wastewater-flow data as a proxy to understand both water and wastewater systems' behaviors during active SDPs. Enabled by a random-effects model of wastewater flow in an urban metropolitan city in Texas, we explore the impacts of various SDPs (e.g., stay home-work safe, reopening phases) using daily flow data gathered between March 19, 2019, and December 31, 2020. Results indicate an increase in residential flow that offset a decrease in nonresidential flow, demonstrating a spatial redistribution of wastewater flow during the stay home-work safe period. Our results show that the three reopening phases had statistically significant relationships to wastewater flow. While this yielded only marginal net effects on overall wastewater flow, it serves as an indicator of behavioral changes in water demand at sub-system spatial scales given demand-flow interdependencies. Our assessment should enable utilities without smart meters in their water system to proactively target their operational response during pandemics, such as (1) monitoring wastewater-flow velocity to alleviate potential blockages in sewer pipes in case of decreased flows, and (2) closely investigating any consequential water-quality problems due to decreased demands.

Impacts of COVID-19 social distancing policies on water demand: A population dynamics perspective.

Social distancing policies (SDPs) implemented in response to the COVID-19 pandemic have led to temporal and spatial shifts in water demand across cities. Water utilities need to understand these demand shifts to respond to potential operational and water-quality issues. Aided by a fixed-effects model of citywide water demand in Austin, Texas, we explore the impacts of various SDPs (e.g., time after the stay home-work safe order, reopening phases) using daily demand data gathered between 2013 and 2020. Our approach uses socio-technical determinants (e.g., climate, water conservation policy) with SDPs to model water demand, while accounting for spatial and temporal effects (e.g., geographic variations, weekday patterns). Results indicate shifts in behavior of residential and nonresidential demands that offset the change at the system scale, demonstrating a spatial redistribution of water demand after the stay home-work safe order. Our results show that some phases of Texas's reopening phases had statistically significant relationships to water demand. While this yielded only marginal net effects on overall demand, it underscores behavioral changes in demand at sub-system spatial scales. Our discussions shed light on SDPs' impacts on water demand. Equipped with our empirical findings, utilities can respond to potential vulnerabilities in their systems, such as water-quality problems that may be related to changes in water pressure in response to demand variations.

Resilient Water and Wastewater Infrastructure Systems through Integrated Humanitarian-Development Processes: The Case of Lebanon's Protracted Refugee Crisis.

When populations are displaced, say after a hurricane or a man-made crisis, water and wastewater utilities can face a real challenge in providing services to those displaced. The challenge is especially difficult when the local infrastructure was already strained in trying to meet the host community's pre-displacement demand. What most communities need are resilient water and wastewater infrastructure systems, and what we develop in this paper is an integrated approach that can achieve such systems. Our approach takes into account the operating environment of bridging what some call the humanitarian-development (HD) nexus. The HD nexus is the phase in which a community transitions toward a response paradigm that combines humanitarian response with long-term services. The HD nexus poses inherent contextual challenges, and we identify them, through interviews with municipalities in Lebanon, in their physical, social, financial, and institutional dimensions. Furthermore, we explore interactions that can inform how best to address these challenges. Our results introduce policy areas (i.e., utility pricing and establishing shared development priorities) that support this transition across the HD nexus and achieve resilient systems. Our discussions give rise to an empirical understanding of the infrastructures' operating environments and thus contribute to global conversations on sustainable development.

Construction waste generation estimates of institutional building projects: Leveraging waste hauling tickets.

The large proportions of waste generated from the construction industry have led to adverse environmental and socio-economic impacts, and as such, there is a need to promote construction waste management (CWM) practices. However, there is limited information regarding construction waste (CW) generated from nonresidential buildings available to support such CWM practices. This study seeks to quantify CW generated from a nonresidential institutional building project using CW data collected from 535 waste hauling tickets. These tickets provide a waste generation summary itemized by various CW streams. The CW data was collected through coordination with the project waste management team. Findings reveal that concrete/masonry is the highest waste stream during the Foundation (46% of CW) and Structural Concrete (88% of CW) stages. During the Masonry Work and Finishing Stage, wood is found to be the primary contributor (54% of CW), followed by concrete/masonry (35.3% of CW). The estimated CW generation rate was approximately 69 kg/m2, with the concrete/masonry and wood waste streams having the highest generation rates of 33.61 kg/m2 and 28.21 kg/m2, respectively, comprising 90% of the total CW across the project. Due to the temporal characteristics of different waste streams, varying CW dumpsters available onsite is recommended based on the construction stage, to mitigate space shortage impacts. Furthermore, this study quantifies the benefits of recycling CW as environmental savings of trees, water, energy and greenhouse gas emissions. Findings of this study would help refine the accuracy of the reported estimates of CW generation for nonresidential construction projects. By shedding light on the corresponding handling practices of the generated waste and project management processes applied on institutional projects, the study can also serve as a guide to better plan for and coordinate the management of CW of these projects. Results may be used to promote and encourage the adoption of CWM practices at the site level to improve the sustainability performance of the construction industry.

BIM-based automated construction waste estimation algorithms: The case of concrete and drywall waste streams.

Globally, the growth of construction activities over the past years has resulted in large quantities of waste generation. Much of this waste is not reused or recycled and is subsequently redirected to landfills. The environmental impact of construction waste (CW) generation and the shortage of land resources for the creation of new landfills have reinforced the need to adopt more innovative CW management practices. Estimation of CW is a necessary step for the adoption of CW management practices. In this study, Building Information Modeling (BIM) is used to automate CW quantification. In this context, CW generation is estimated as the materials purchased but not incorporated into the actual building structure. Algorithms developed to quantify concrete and drywall waste streams are presented to demonstrate the proposed CW estimation method. The proposed concrete algorithm is validated by comparing estimated waste to actual waste data reported in the waste hauling tickets of a real-world project. Furthermore, CW generation quantities reported in the literature are used to validate estimates of both concrete and drywall waste streams. By leveraging material quantities directly from BIM-as opposed to manual estimations-CW estimation can be streamlined, enabling decision makers to implement more efficient construction waste management practices in the field.