Measuring non-road diesel emissions in the construction industry: a synopsis of the literature

Emission characteristics of particulate matter emitted by typical off-road construction machinery

Off-road machinery, especially construction equipment, is one of the most important pollutant sources of the deterioration in the air quality of Chinese urban areas owing to its large quantity and to the absence of stringent emission requirements. In this study, we used a portable emission measurement system (PEMS) to measure the exhaust pollutant emission characteristics for 13 pieces of construction machinery including excavators and hydraulic crushers under different working conditions, such as real-world operating condition, free accelerating condition, and overloading condition, innovatively adopting the method of synchronizing video recording and emission measurement to divide the operation process of construction machinery under different working conditions into different action stages. In addition, the relationship between the emission characteristics and the maintenance history of 13 pieces of construction machinery was analyzed. The present study exploits this recent progress to enrich the measurement method of off-road mobile machinery light absorption coefficient (LAC), which does not depend on the measurement environment and the type of equipment. There are three significant findings from the study that can be noted. To begin with, the exhaust LAC in the excavation operation stage was the highest in the real-world operating condition. The main reason for it is that the engine load was suddenly increased during this stage; due to the response lag characteristics of the turbocharger, the intake charge was delayed, so the quality of oil and gas mixture in the cylinder becomes worse, and the concentration of local mixture is higher. Therefore, insufficient combustion of the mixture leads to a large amount of soot formation. Second, when the real-world operation is unavailable, the free accelerating condition was not applicable, but the overflow loading condition is suitable for replacing the real-world operating condition to measure the excavator exhaust LAC. Finally, as for in-use construction machinery, regular maintenance is an effective measure to reduce the engine exhaust LAC. Our findings contribute to improving the efficiency and accuracy of the environmental protection department's evaluation of the exhaust LAC of off-road mobile machinery and promoting the application of different technologies for high-emission off-road mobile machinery to reduce pollutant emissions so that it can continue to be used.

Assessment of Emissions and Energy Consumption for Construction Machinery in Earthwork Activities by Incorporating Real-World Measurement and Discrete-Event Simulation

Earthwork, an essential activity in most construction projects, consumes large quantities of fossil fuel and produces substantial air pollution with adverse environmental impacts. To achieve more sustainable construction processes, novel methodologies to evaluate and improve the performance of earthwork operations are required. This study quantifies the real-world emissions and fuel consumption of construction equipment within an earthwork project in China. Two wheel loaders and two dump trucks are examined through on-board measurements and in-lab engine tests. The duty cycles of construction equipment are categorized with respect to their power efficiency and working patterns. Moreover, the power-specific and time-based emission factors for these duty cycles are computed and compared with relevant legislative emission limits. Significant emission variations among different duty cycles were found, and the real-world emission measurements exceeded the results from the in-lab test required for emission certification. In addition, a discrete-event simulation (DES) framework was developed, validated, and integrated with the computed emission factors to analyze the environmental and energy impacts of the earthwork project. Furthermore, the equipment fleet schedule was optimized in the DES framework to reduce greenhouse gas emissions and fuel consumption by 8.1% and 6.6%, respectively.

Problems and Directions in Creating a National Non-Road Mobile Machinery Emission Inventory: A Critical Review

Greenhouse emissions and air pollutants pose a global threat to the environment and human health. Emission inventories are a valuable tool in understanding emission sources and their overall impact on the environment. Most cities and countries do not include non-road mobile machinery (NRMM) when compiling emission inventories. Furthermore, little effort has been made to understand better the impact of this source of emissions on the environment. For these reasons, this research examines the data from the existing NRMM emission inventories and other studies concerning NRMM emissions. After careful literature review, three main problems in creating a national NRMM emission inventory are identified and reviewed: lack of a comprehensive list of NRMM and their activity data, lack of emission factor data, and lack of research. The data from the existing inventories show that compared to some emissions, NRMM has a three times larger proportion of emissions compared to the proportion of energy consumption. Furthermore, there are significant differences in total emissions when comparing the same pollutants among different countries. A general lack of data is the common denominator for all these problems and can only be solved by creating national NRMM databases operated by a designated institution. This institution must be able to annually update relevant NRMM data through questionnaires and experimental research on the existing NRMM.

Fossil fuels consumption and carbon dioxide emissions in G7 countries: Empirical evidence from ARDL bounds testing approach

This research determines the intertemporal relationships caused by the coal, oil, and natural gas consumption in the carbon dioxide emission by the G7 countries from 1965 to 2018. Auto-regressive and Distributed Lags models and Bound test were used to detect cointegration and understand the dynamic effect. Due to structural breaks occurred in the variables, two dummy variables for the periods of breaks, 1978 and 1990 were incorporated respectively. Positive causality was identified, in the sense that the consumption of fossil fuels provides an increase in carbon dioxide emissions. Short-term elasticities indicate that an increase of 1 percentage point in the consumption of oil, coal, and natural gas will cause, respectively, an increase of 0.4823%, 0.3140%, and 0.1717% in carbon dioxide emissions. In the long run, the increase of 1 percentage point in the consumption of oil, coal, and natural gas will cause, respectively, an increase of 0.4924%, 0.2692%, and 0.1829% in carbon dioxide emissions. The error correction model (ECM = -0.4739) indicates that 47.39% of a shock in the carbon dioxide emissions variable is resolved in one year and after 2 years, carbon dioxide emissions return to long term equilibrium.

Real-world emissions of construction mobile machines and comparison to a non-road emission model

This study implemented real-world tests in Nanjing, China for measuring emission factors (EFs) of air pollutants, including Carbon Monoxide (CO), Hydrocarbon (HC), Nitrogen Oxides (NO_x), and Particulate Matter (PM) from ten construction machines in three operational modes (idling, moving, and working) with a Portable Emission Measurement System. The idling mode shows the least variation of EFs, and its average CO EFs can be higher than the moving and working modes by 43% and 34%, respectively. The working mode generates the highest emission for all other pollutants with the highest variation. The EFs suggested by the Guide (an official guidebook for developing emission inventory in China) are in general lower than the measured EFs, and the gap becomes larger for older machines. The EFs of CO, NO_x, and PM of China Stage II machines are 24%, 120%, and 66% higher than those of the Guide, respectively. The differences go up as high as 126%, 1066%, and 559% for China Stage I machines, indicating the upgrade of engine technology from Stage I to Stage II, as well as the effect of machine deterioration. The result of this study reveals the effectiveness of stringent emission standards in controlling emissions from construction machines. High emissions from older machines emphasize the importance of a more rigorous machine replacement policy and a regulated maintenance strategy. The result also stresses the need to update the Guide with differentiated activity modes, region variations, and machine deterioration effects.

Digital Twin and CyberGIS for Improving Connectivity and Measuring the Impact of Infrastructure Construction Planning in Smart Cities

Smart technologies are advancing, and smart cities can be made smarter by increasing the connectivity and interactions of humans, the environment, and smart devices. This paper discusses selective technologies that can potentially contribute to developing an intelligent environment and smarter cities. While the connectivity and efficiency of smart cities is important, the analysis of the impact of construction development and large projects in the city is crucial to decision and policy makers, before the project is approved. This raises the question of assessing the impact of a new infrastructure project on the community prior to its commencement—what type of technologies can potentially be used for creating a virtual representation of the city? How can a smart city be improved by utilizing these technologies? There are a wide range of technologies and applications available but understanding their function, interoperability, and compatibility with the community requires more discussion around system designs and architecture. These questions can be the basis of developing an agenda for further investigations. In particular, the need for advanced tools such as mobile scanners, Geospatial Artificial Intelligence, Unmanned Aerial Vehicles, Geospatial Augmented Reality apps, Light Detection, and Ranging in smart cities is discussed. In line with smart city technology development, this Special Issue includes eight accepted articles covering trending topics, which are briefly reviewed.

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

Green Performance Evaluation System for Energy-Efficiency-Based Planning for Construction Site Layout

The location of temporary facilities in a construction project and the entire site layout plan directly affect project objectives such as time, labor cost, and material transportation and handling. The layout of construction sites also affects entrainment factors such as energy consumption, carbon footprints, and overall construction operation productivity. While site layout planning has been intensively investigated from a project objectives perspective, there have been very few studies of energy-efficiency-based planning, or of the sustainability performance of site layouts. This study developed a green performance evaluation system aimed at improving the sustainability of construction site layouts. The identified factors include six sustainable evaluation categories covering energy conservation and environmental protection, people-oriented principles, construction efficiency, intensity of economic growth, intensity of space use, and the overall control of process. An analytic hierarchy process (AHP) was adopted to determine the weight of each attribute and a fuzzy comprehensive evaluation method was established to carry out the evaluation. The 23 attributes adopted in this paper were identified in the literature; however, the major contribution of this paper is the development of a green performance evaluation system. This system integrates both qualitative and quantitative attributes and provides an overall evaluation of the environmental effectiveness of a construction site layout. The proposed evaluation system was validated with a commercial building project. The average utilization ratio of the case study site was calculated as 94%, and lessons learned are discussed in this paper. The case study analysis identified available site spaces around the building and examined how the arrangement of resources and facilities ensures effective connection between construction activities. The findings showed that the facility’s layout plays a crucial role in energy consumption and green performance. The proposed system will support construction project managers to create high-performance construction site layouts in more scientific and systematic ways.

Predicting the Impact of Climate Change on Thermal Comfort in A Building Category: The Case of Linear-type Social Housing Stock in Southern Spain

The Climate Change scenario projected by the IPCC for the year 2050 predicts noticeable increases in temperature. In severe summer climates, such as the Mediterranean area, this would have very negative effects on thermal comfort in the existing housing stock, given the current high percentage of dwellings which are obsolete in energy terms and house a population at serious risk of energy poverty. The main aim of this paper is to generate a predictive model in order to assess the impact of this future climate scenario on thermal comfort conditions in an entire building category. To do so, calibrated models representing linear-type social multi-family buildings, dating from the post-war period and located in southern Spain, will be simulated extensively using transient energy analyses performed by EnergyPlus. In addition, a sensitivity analysis will be performed to identify the most influential parameters on thermal discomfort. The main results predict a generalized deterioration in indoor thermal comfort conditions due to global warming, increasing the average percentage of discomfort hours during the summer by more than 35%. This characterization of the future thermal behaviour of the residential stock in southern Spain could be a trustworthy tool for decision-making in energy retrofitting projects which are so badly needed. To do so, further work is required on some limitations of this model so that different user profiles and typologies can be represented in detail and an economic assessment can be included.

Methods for monitoring construction off-road vehicle emissions: a critical review for identifying deficiencies and directions

The paper reviews the existing applications of sensing technologies for measuring construction off-road vehicle emissions (COVE) such as earthmoving equipment. The current literature presented different measurement methods and reported the results of utilisation of new technologies for measuring COVE. However, previous papers used different technology applications covering only a part of the monitoring process with its own limitations. Since technologies are advancing and offering novel solutions, there is an urgent need to identify the gaps, re-evaluate the current methods, and develop a critical agenda for automating the entire process of collecting emissions data from construction sites, and monitoring the emission contributors across cities. This paper systematically identifies relevant papers through a search of three key databases-Web of Science, Engineering Valley and Scopus-covering the publications in the last decade from 2008 to 2017. An innovative robust research method was designed to select and analyse the relevant papers. The identified papers were stored in a data set, and a thematic algorithm employed to find the clusters of papers which might be potentially relevant. The selected papers were used for further micro-thematic analysis to find key relevant papers on COVE, and the gap in the literature. A sample of relevant papers was found relevant to COVE and critically reviewed by coding and content analysis. This paper critically reviews the selected papers and also shows that there is a considerable gap in the applications of new technologies for measuring in-use COVE in real time based on real activities toward automated methods. This review enables practitioners and scholars to gain a concrete understanding of the gap in measuring COVE and to provide a significant agenda for future technology applications.