Operational level emissions modelling of on-road construction equipment through field data analysis

Barati, Khalegh; Shen, Xuesong

doi:10.1016/j.autcon.2016.08.010

Cited by 26 publications

(18 citation statements)

References 14 publications

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“…For loader and excavator, emission rates in loading mode are much higher due to more power of engine consumed. In the operation-level emission model developed by Barati et al, it was verified that there is a highly correlated relationship between the amount of used power of engine and emission rates [14]. It is also noteworthy that the emission rates in idling and dumping operation modes are relatively the same and minimum for both pieces of equipment considered in the study.…”

Section: Case Studymentioning

confidence: 75%

“…The engine load of most construction equipment ranges approximately from 20% to 80% of the maximum engine power [13]. Barati et al [14] showed a highlycorrelated direct linear relationship between engine load and emission rates. Normally, construction equipment uses one certain type of fuel in their lifetime which has negligible changes in ingredients.…”

Section: Affecting Parameters On Emissionsmentioning

confidence: 99%

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Emissions Modelling of Earthmoving Equipment

Barati¹,

Shen²

2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Earthmoving operations produce high amount of emissions and are one of the main sources of air pollutants in construction and mining industries. Modelling and quantifying the emissions produced by earthmoving equipment is the first step for developing emissions reduction schemes. Currently, emissions of construction and mining equipment are mainly estimated through simulation or laboratory tests which may not represent the real-world situations. This paper presents a comprehensive methodology to predict emission rates of carbon dioxide (CO2), carbon monoxide (CO), hydrocarbon (HC) and nitrogen oxides (NOx) of earthmoving equipment by considering operation modes and engine attributes. The developed framework includes three main processes of instrumentation, data collection and data analysis. Two instruments of portable emission measurement system (PEMS) and GPS aided inertial navigation system (GPS-INS) are proposed for conducting field experiments and collecting emission rates and operational parameters. Further, site observation is conducted to estimate the cycle time and time ratio of operation modes. An exploratory analysis method is then developed to process the gathered data and model emissions at operation and equipment level. The applicability of the developed methodology is finally verified through experimenting and modelling emissions of one loader and one excavator.

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Section: Case Studymentioning

confidence: 75%

Section: Affecting Parameters On Emissionsmentioning

confidence: 99%

Emissions Modelling of Earthmoving Equipment

Barati¹,

Shen²

2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…The new industrialization of construction can effectively boost the supply of labor and improve production efficiency. BIM methodology could reduce carbon emissions and mitigate the effects of climate change by enhancing building energy efficiency and reducing total energy consumption [39][40][41][42][43][44]. On the other hand, it can enhance the technological level of architectural production, reduce pollution in the construction process, and improve the safety and quality of construction projects.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Characteristics and Influencing Factors of China’s Construction Industry Carbon Intensity

Du¹,

Wu²,

Wang³

et al. 2017

Pol. J. Environ. Stud.

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Climate change continuously threatens human and ecological environments. As the extravagant greenhouse gas (GHG) emissions are not conducive to preserve the Earth's climate and temperature, many national and international entities have made commitments to minimize the carbon footprint [1]. As the largest energy consumer and carbon emitter in the world, China is facing increasing pressure on cutting its CO 2 emissions down [2][3]. In this context, the Chinese government released a mitigation action plan to reduce carbon emission intensity by 40 to 45% in 2020 based on 2005 levels [4]. Because of the massive utilization of materials Pol. J. Environ. Stud. Vol. 26, No. 6 (2017), 2507-2521 AbstractClimate change continuously threatens sustainable development. As the largest energy consumer and carbon emitter in the world, China is facing increasing pressure to cut carbon emissions. Based on Moran's index I and geographically weighted regression, this paper investigates the spatiotemporal characteristics and the dominating factors of China's province-level carbon intensity in the construction industry from 2005 to 2014, which is aimed at providing a scientific basis for government while implementing a regionaloriented carbon emissions reduction strategy. The empirical results are shown as follows. Firstly, carbon intensity in the construction industry of each province has been decreasing in the past 10 years. Secondly, provincial carbon intensity in this sector shows significant positive spatial autocorrelation characteristics and the degree of spatial clustering of carbon intensity tended to weaken in this period. Third, according to the analysis of the geographically weighted regression (GWR) model, carbon intensity is positively affected by energy intensity while the labor input and production efficiency both have negative effect. Particularly the regression coefficient of labor input is almost twice as large as the other two factors. The results reveal that there is a significant spatial disparity of these three factors in different provinces.

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“…In addition, it is estimated that construction industry produces more than 100 million tons of carbon dioxides (CO 2 ) annually, and contributes to around 5% of global CO 2 emissions which is ranked as the third CO 2 emitter per utilized unit of energy after cement and steel production sectors [3]. According to the United Nation Framework Convention on Climate Change (UNFCCC), GHG emissions from construction operations account for around 6.8% of the total emissions produced by all industrial sectors [4]. The majority of fuel used and emissions produced in the construction sector is related to equipment operations.…”

Section: Introductionmentioning

confidence: 99%

Modelling Traffic Conditions on Fuel Use and Emissions of On-road Construction Equipment

Barati¹,

Shen²

2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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The consumption of fossil fuels by on-road vehicles is a main source of air pollution specifically greenhouse gases (GHGs) worldwide. The construction industry, due to the use of a large number of heavy-duty equipment including haulage trucks, contributes to a significant amount of fuel consumption and consequent emissions production. Due to increasing number of vehicles on the road, traffic condition is becoming one of the main variables having a considerable impact on the fuel use and emissions of such vehicles. There is a lack of comprehensive studies in construction field quantifying the effect of road traffic on fuel use and emissions of construction equipment. This research aims to model the impact of traffic conditions on fuel use and emissions of on-road construction vehicles. The research framework is first developed to present the methodology of data collection and analysis, and then introduce the fuel use and emissions models applied in predicting the effect of traffic conditions. Three variables of driving speed, idling time and equipment stop are identified as representatives of traffic conditions, and their impacts on fuel use and emissions are quantified through conducting statistical analyses on collected field data. The achieved results found that by having more effective traffic management and planning, the fuel cost and consequent emissions can be reduced up to 9% due to the decreasing idling time of equipment. It is also indicated that by lowering the number of equipment stops, up to 0.66 l/100kW fuel is saved and up to 1.7 kg/100kW CO 2 is emitted less per each stop.

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Operational level emissions modelling of on-road construction equipment through field data analysis

Cited by 26 publications

References 14 publications

Emissions Modelling of Earthmoving Equipment

Emissions Modelling of Earthmoving Equipment

Spatiotemporal Characteristics and Influencing Factors of China’s Construction Industry Carbon Intensity

Modelling Traffic Conditions on Fuel Use and Emissions of On-road Construction Equipment

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