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Volatile organic compounds (VOCs) in urban areas are of great interest due to their significant role in forming ground-level ozone and adverse public health effects. Emission inventories usually compile the outdoor VOCs emission sources (e.g., traffic and industrial emissions). However, considering emissions from volatile chemical products (e.g., solvents, printing ink, personal care products) is challenging because of scattered data and the lack of an effective method to estimate the VOCs emission rate from these chemical products. This paper aims to systematically analyse potential sources of VOCs emission in Canada's built environment, including volatile chemical products. Also, spatial variation of VOCs level in the ambient atmosphere is examined to understand the VOC relationship with ozone and secondary organic aerosol formation. The study shows that VOCs level may vary among everyday microenvironments (e.g., residential areas, offices, and retail stores) depending on the frequency of product consumption, building age, ventilation condition, and background ambient concentration in the atmosphere. However, it is very difficult to establish VOC speciation and apportionment to different volatile chemical products that contribute most significantly to exposure and target subpopulations with elevated levels. Thus, tracer compounds can be used to identify inventory sources at the consumer end. A critical overview highlights the limitations of existing VOC estimation methods and possible approaches to control VOC emissions. The findings provide crucial information to establish an emission inventory framework for volatile chemical products at a national scale and enable policymakers to limit VOCs emission from various volatile chemical products.

PurposeThe purpose of this study is to minimize cost and minimize building condition. Weibull distribution approach was employed to generate deterioration curves over time. The third floor of Concordia University’s Engineering And Visual Arts (EV) Complex in Montreal, Canada, served as a case study to test the maintenance model and determine the optimal maintenance activities to be performed.Design/methodology/approachThis research has demonstrated that there is insufficient fund allocation for the maintenance of non-residential buildings. Therefore, this research focused on designing and developing a maintenance optimization model that provides the type of spaces (architectural system) in a building. Sensitivity analysis was used to calculate weights to validate the model. Particle swarm optimization, based explicitly on multiple objectives, was applied for the optimization problem using MATLAB.FindingsFollowing 100 iterations, 13 non-dominant solutions were generated. Not only was the overall maintenance cost minimized, but the condition of the building was also maximized. Moreover, the condition prediction model demonstrated that the window system type has the most rapid deterioration in educational buildings.Originality/valueThe model is flexible and can be modified by facility managers to align with the required codes or standards.

Purpose This paper extends the authors’ previous research work investigating resilience for municipal infrastructure from an asset management perspective. Therefore, this paper aims to formulate a pavement resilience index while incorporating asset management and the associated resilience indicators from the authors’ previous research work. Design/methodology/approach This paper introduces a set of holistic-based key indicators that reflect municipal infrastructure resiliency. Thenceforth, the indicators were integrated using the weighted sum mean method to form the proposed resilience index. Resilience indicators weights were determined using principal components analysis (PCA) via IBM SPSS®. The developed framework for the PCA was built based on an optimization model output to generate the required weights for the desired resilience index. The output optimization data were adjusted using the standardization method before performing PCA. Findings This paper offers a mathematical approach to generating a resilience index for municipal infrastructure. The statistical tests conducted throughout the study showed a high significance level. Therefore, using PCA was proper for the resilience indicators data. The proposed framework is beneficial for asset management experts, where introducing the proposed index will provide ease of use to decision-makers regarding pavement network maintenance planning. Research limitations/implications The resilience indicators used need to be updated beyond what is mentioned in this paper to include asset redundancy and structural asset capacity. Using clustering as a validation tool is an excellent opportunity for other researchers to examine the resilience index for each pavement corridor individually pertaining to the resulting clusters. Originality/value This paper provides a unique example of integrating resilience and asset management concepts and serves as a vital step toward a comprehensive integration approach between the two concepts. The used PCA framework offers dynamic resilience indicators weights and, therefore, a dynamic resilience index. Resiliency is a dynamic feature for infrastructure systems. It differs during their life cycle with the change in maintenance and rehabilitation plans, systems retrofit and the occurring disruptive events throughout their life cycle. Therefore, the PCA technique was the preferred method used where it is data-based oriented and eliminates the subjectivity while driving indicators weights.

Several research studies have ranked indoor pollution among the top environmental risks to public health in recent years. Good indoor air quality is an essential component of a healthy indoor environment and significantly affects human health and well-being. Poor air quality in such environments may cause respiratory disease for millions of pupils around the globe and, in the current pandemic-dominated era, require ever more urgent actions to tackle the burden of its impacts.\ud The poor indoor quality in such environments could result from poor management, operation, maintenance, and cleaning. Pupils are a different segment of the population from adults in many ways, and they are more exposed to the poor indoor environment: They breathe in more air per unit weight and are more sensitive to heat/cold and moisture. Thus, their vulnerability is higher than adults, and poor conditions may affect proper development.\ud However, a healthy learning environment can reduce the absence rate, improves test scores, and enhances pupil/teacher learning/teaching productivity. In this article, we analyzed recent literature on indoor air quality and health in schools, with the primary focus on ventilation, thermal comfort, productivity, and exposure risk. This study conducts a comprehensive review to summarizes the existing knowledge to highlight the latest research and solutions and proposes a roadmap for the future school environment. In conclusion, we summarize the critical limitations of the existing studies, reveal insights for future research directions, and propose a roadmap for further improvements in school air quality. More parameters and specific data should be obtained from in-site measurements to get a more in-depth understanding at contaminant characteristics. Meanwhile, site-specific strategies for different school locations, such as proximity to transportation routes and industrial areas, should be developed to suit the characteristics of schools in different regions. The socio-economic consequences of health and performance effects on children in classrooms should be considered. There is a great need for more comprehensive studies with larger sample sizes to study on environmental health exposure, student performance, and indoor satisfaction. More complex mitigation measures should be evaluated by considering energy efficiency, IAQ and health effects.

PurposeThis study highlights the findings of an empirical study to investigate waste factors (WFs) affecting the performance and delivery of construction projects in developing countries. The objectives of this study are to identify non-physical WFs in developing nations and rank the identified factors based on their degree of influence on the key performance indicators (KPIs) of cost, quality and time.Design/methodology/approachIn total, 34 WFs were identified through a detailed literature review and consolidated using semi-structured interviews with construction practitioners. The statistical analysis involved a normality test using the Shapiro–Wilk test to determine if sample data have been drawn from a normally distributed population, ranking the WFs using the Frequency Index (FI), Severity Index (SI) and Importance Index (IMPI), ranking the WFs based on their effect on the project KPIs of cost, quality and time, and identify clustering structures for the identified WFs to using factor analysis (FA).FindingsThe results revealed ineffective planning and scheduling, rework/repair of defective work and resource quality problems (human, material and equipment) as the three most important WFs affecting construction projects. The factor analyses showed that WFs can be grouped into five interrelated components, suggesting the need for integrated and holistic strategies to overcome the identified WF.Practical implicationsUnderstanding the effects of WFs on construction projects is a first step towards designing holistic solutions to ensuring projects deliver value to the clients and other stakeholders. The findings of this study provide direction to construction practitioners on where to focus appropriate strategies to manage the identified WFs effectively and, therefore, improve the productivity of construction projects.Originality/valueThis study provides the first holistic analysis of WFs affecting the productivity of construction projects in developing countries.

According to the Canada infrastructure report 2016, 62.6% of roads in Canada are in a good condition, nevertheless, with current investment rates, significant road networks will suffer a decline in their condition and will be vulnerable to sudden failure. Accordingly, this paper tackles the pavement resilience from an asset management perspective and aims at developing a resilience-based asset management framework for pavement networks. This was carried out through the development of five components: (i) a central database of asset inventory, (ii) a pavement condition and level of service assessment models, (iii) a regression of the effect of freeze–thaw on pavement network, (iv) a financial and temporal models, and (v) an optimization model to formulate the mathematical denotation for the proposed resilience assessment approach and integrate the above components. The model results were promising in terms of maintaining pavement resiliency by selecting a near optimal intervention plan that meets the municipality limitations.

Conventional approaches towards energy-system modelling and operation are based upon the system design and performance optimization. In system-design optimization, the thermal or mechanical characteristics of the systems providing for the heat or electricity demands were derived separately without integration with the energy source and without interaction with demand, which results in low-efficiency energy performance. This paper presents a key review on the integration of biomass-powered combined heat and power (BCHP) systems in district-heating systems as well as coupling with thermal-energy storage. In BCHP design, the appropriate sizing of the associated components as part of the district-heating system is very important to provide the optimal dispatch strategy as well as minimized cost and environmental impact while it co-operates with thermal-energy storage. Future strategies for the feasibility, evaluation and integration of biomass-powered energy systems in the context of district systems are also studied.

Transmission pipelines comprise a major part of a gas network, conveying natural gas within jurisdictions, and across international boundaries. In the United States, more than 10,000 failure incidents have been reported in gas transmission pipelines in a 20-year period from 1996 to 2016 leading to a cumulative property damage of more than $748 million. Among different failure sources, corrosion is ranked as the most frequent one, corresponding to approximately a quarter of total failures. Though in-line inspection is counted as the most frequently applied corrosion monitoring technique for oil and gas pipelines, it imposes considerable costs due to the necessity of implementing frequent inspections using smart devices. For this reason, several failure prediction models have been developed to estimate the corrosion failure. However, the majorities of these prediction models rely solely on experimental tests or limited historical records which undermine the extent of their applicability and ignore pipeline environmental and geographical circumstances. The objective of this research is to develop failure prediction models for external corrosion in underground gas transmission pipelines by considering both conventional and environmental/geographical variables. For this objective, multiple regression analysis was performed on the accessible historical data reported for gas transmission pipelines. Two main climate regions of Great Plains and South East in the US were selected, and their corresponding failure prediction models were developed. Such development was based on a step by step procedure analyzing different scenarios. Considering diagnostic measures, null hypothesis and residual analysis, scenario 3 was selected as satisfactory. The validation tests of the developed models present a root mean square error (RMSE) of 0.04 and 0.07 and R-Sq of 0.93 and 0.75, respectively. The results of this research can be applied in maintenance planning of gas transmission pipeline to estimate the critical time in which a pipeline may encounter external corrosion failure, and to accordingly schedule the maintenance activities.

Construction projects result in the generation of wastes, which can be broadly classified into physical and non-physical wastes. Non-physical wastes present a different challenge than physical wast...

Lean construction (LC) targets the reduction and eventual removal of non-value adding activities (otherwise known as waste) in construction projects to increase value to stakeholders. The applicati...

Solid biomass combustors are being increasingly deployed for energy supply to help reduce global greenhouse gas emissions, despite the fact that they still suffer from some uncontrolled deflections...

Biomass has great potential to meet greenhouse gas reduction and fuel supply security in the future. Although the grate biomass combustors are increasingly deployed worldwide to generate energy from solid biomass, challenges in understanding the system operation to some extent have remained. This paper analyzes the effects of fuel composition uncertainty on the biomass grate combustor’s performance, which have not been solved so far. A 1D transient numerical model of the biomass fuel bed combustion is developed. A set of thermal gravimetric analysis (TGA) experiments on randomly selected biomass particles from the same fuel supplier are conducted to achieve the proximate analysis of the particles. The Bayesian model averaging (BMA) method was exercised to deliver the fuel uncertainty into the CFD model of the fuel bed. Results revealed that the fuel composition variability can significantly affect the solid fuel conversion so that ignoring them can result in incomplete combustion. In three various scenarios proposed, combustor is analyzed: (I) using primary fuel composition given by the producer, (II) mean value of fuel composition obtained from the BMA model, and lastly (III) fuel composition under fully uncertainty conditions. Results revealed that overlooking the fuel uncertainty results in overestimating system energy output by 8.3% and also can waste 1611-kg feed annually which is roughly 5% of whole consumed fuel. Meanwhile, owing to uncertainty associated with fuel composition, flame temperature can fluctuate up to 15 °C. According to the uncertainty analysis, char content of wood pellets has dominating role in fuel quality. Finally, a life cycle analysis (LCA) is conducted for the first, second, and coal-fueled system scenarios.

Construction workspace management has been a big issue in research and practice in recent years due to the need to improve productivity and safety by reducing spatio-temporal clashes in the managem...

Purpose One of the most critical infrastructures is a data center (DC) because of it having many servers, computers and other equipment. DCs provide online services for various companies in the information technology (IT) industry. DC facilities should provide reliable online services while addressing the required quality and performance level considering maximum reliability and availability. The purpose of this study is to represent and classify the main findings in this area and to identify the main research gaps and shortcomings from the perspective of research. Design/methodology/approach This paper provides an organized and systematic literature review focusing on topics regarding the operation and maintenance (O&M) management of DCs. Findings Although there are several studies on O&M management systems for industrial systems and facilities, a limited number of studies with few methods and models have focused on DCs so far and these facilities require more attention. This paper identifies the issues and challenges for DC buildings and facilities and provides a conclusion of the findings to highlight the main research limitations for discovering new potential methods as future research opportunities. Research limitations/implications The paper has highlighted the main practical issues of DCs in terms of maintenance management. Several research works have been discussed specifically for DC’s maintenance, which makes this paper a credible source for researchers, maintenance managers and companies involved in the area of DC. Because several of the reviewed literature were based on real case studies, decision-makers in the DC maintenance sector can take advantage of new research on maintenance scheduling to reduce the costs of maintenance. Originality/value The paper has presented a comprehensive list of frequent keywords in recent publications related to O&M management for DCs. It has provided a categorized list of publications based on by their topic, methodology and case study. Because this paper has discussed research works specifically for DC’s maintenance, it is a credible source for researchers, maintenance managers and companies involved in the area of DCs.

This paper proposes an availability-based maintenance scheduling for a vibrating-grate biomass boiler. The main objective is to minimise the maintenance programme cost while maintaining the system ...

Prioritization of maintenance and rehabilitation interventions according to their urgency, expected improvements, costs, and downtimes is a necessary task in facility management. This is pa...

This article provides a state-of-the-art review on emerging applications of smart tools such as data analytics and smart technologies such as Internet-of-things (IoT) in case of design, management and control of energy storage systems. In particular, we have established a classification of the types and targets of various predictive analytics for estimation of load, energy prices, renewable energy inputs, state of the charge, fault diagnosis, etc. In addition, the applications of information technologies, in particular, use of cloud, IoT systems, building management systems (BMS) and building information modeling (BIM) and their contributions tomanagement of energy storage systems will be reviewed in details. The paper concludes by highlighting the emerging issues in smart energy storage systems and providing directions for future research. This article provides a state-of-the-art review on emerging applications of smart tools such as data analytics and smart technologies such as internet-of-things in case of design, management and control of energy storage systems. In particular, we have established a classification of the types and targets of various predictive analytics for estimation of load, energy prices, renewable energy inputs, state of the charge, fault diagnosis, etc. In addition, the applications of information technologies, and in particular, use of cloud, internet-of-things, building management systems and building information modeling and their contributions to management of energy storage systems will be reviewed in details. The paper concludes by highlighting the emerging issues in smart energy storage systems and providing directions for future research.

Improving energy performance of buildings is of particular importance in new construction and existing buildings. Building refurbishment is considered a practical pathway towards energy efficiency as the replacement of older buildings is at a slow pace. There are various ways of incorporating energy conservation measures in buildings through refurbishment projects. As such, we have to choose among various passive or active measures. In this study, we develop an integrated assessment model to direct energy management decisions in retrofit projects. Our focus will be on alternative passive measures that can be included in refurbishment projects to reduce overall energy consumption in buildings. We identify the relative priority of these alternatives with respect to their non-monetary (qualitative) benefits and issues using an analytic network process. Then, the above priorities will form a utility function that will be optimized along with the energy demand and retrofit costs using a multi-objective optimization model. We also explore various approaches to formulate the uncertainties that may arise in cost estimations and incorporate them into the optimization model. The applicability and authenticity of the proposed model is demonstrated through an illustrative case study application. The results reveal that the choice of the optimization approach for a retrofit project shall be done with respect to the extent of variations (uncertainties) in expected utilities (benefits) and costs for the alternative passive technologies.

This Review covers the current state-of-the-art literature dealing with numerical modeling and experimental analysis of moving grate biomass combustors. The greatest attention is paid to the modeli...

Utilizing appropriate materials and assemblies in building envelope components could lead to energy savings, increased durability, and sustainability gains. This study aims at providing an integrat...

Facility managers of hospitals face complex maintenance decisions as they deal with a multitude of maintenance requests in an environment of limited resources and segmented information. Re...

During the normalized phase of COVID-19, droplets or aerosol particles produced by infected personnel are considered as the potential source of infection with uncertain exposure risk. As such, in densely populated open spaces, it is necessary to adopt strategies to mitigate the risk of infection disease transmission while providing sufficient ventilation air. An example of such strategies is use of physical barriers. In this study, the impact of barrier heights on the spread of aerosol particles is investigated in an open office environment with the well-designed ventilation mode and supply air rate. The risk of infection disease transmission is evaluated using simulation of particle concentration in different locations and subject to a number of source scenarios. It was found that a barrier height of at least 60cm above the desk surface is needed to effectively prevent the transmission of viruses. For workstations within 4m from the outlet, a 70cm height is considered, and with a proper ventilation mode, it is shown that the barriers can reduce the risk of infection by 72%. However, for the workstations further away from the outlet (beyond 4m), the effect of physical barrier cannot be that significant. In summary, this study provides a theoretical analysis for implementing physical barriers, as a low-cost mitigation strategy, subject to various height scenarios and investigation of their effectiveness in reducing the infection transmission probability.

Healthcare building infrastructure in Canada is currently facing two problems: Aging and Deferred Maintenance, leading to an increase in unexpected failures causing interruptions in the hospital operation which in turn affects the health and safety of its occupants. Despite the efforts exerted to overcome this, solutions cannot be easily implemented as they are often faced with limited and insufficient funds. Therefore, previous researches have experimented ways targeting a reduction in the rehabilitation costs while sustaining an acceptable physical condition of hospital assets. However, there is more to a building's performance than its physical condition. Hence, this study assesses the hospital performance by including functional parameters of components rather than solely evaluating their physical condition on the basis of an integration between Neutrosophic Logic and Analytic Hierarchy Process, and accordingly improves the rehabilitation decisions by utilizing the output from the previous model as an objective for a genetic algorithm optimization model to prioritize rehabilitation activities within a limited funding allowance. The developed model was validated by applying it to a real hospital situation where the results obtained from the model were compared to the actual output attained from rehabilitation works inside the hospital facility, and the model developed in this study outperformed the current practice by an improvement of 34%. This framework is expected to aid decision-makers in efficiently allocating rehabilitation funds to the most critical hospital building systems which in turn improves the performance and availability of hospital assets.