Emissions of air pollutants from non-road construction machinery in Beijing from 2015 to 2019

Li, Xueyao; Yang, Liu; Liu, Yongteng; Zhang, Chongbo; Xu, Xiaohong; Mao, Hongjun; Jin, Taosheng

doi:10.1016/j.envpol.2022.120729

“…Activity level, a critical parameter in the compilation of emission inventories for non-road mobile machinery, remains a challenging aspect to quantify accurately. Most studies have relied on estimates derived from consultations with machinery owners or relevant industry associations rather than obtaining the actual activity level data [14][15][16]. Liu et al [17] from the Chinese National Automobile Quality Inspection and Testing Center calculated the emission list for Beijing construction machinery based on the power method and found that the uncertainty of the established emission list mainly came from the activity level of the machinery and from the use of rough activity level data rather than the actual activity level data of the machinery.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Emission Inventory for Non-Road Mobile Machinery in Shandong Province: An Analysis Grounded in Real-World Activity Levels

Zhu,

Cai,

Ouyang

et al. 2024

Sustainability

0

View full text Add to dashboard Cite

In tandem with the advancement of urban intelligent technology, the construction of remote monitoring platforms and databases for non-road mobile machinery is gradually improving in various provinces and cities. Employing the remote monitoring platform for non-road mobile machinery enables a detailed big data analysis of the actual operational state of the machinery. This method yields precise data on the activity levels of various machinery types. Importantly, it addresses the issue of reduced accuracy in emission inventories, which often arises from the conventional practice of using standard recommended values from the Guide to determine machinery activity levels during the compilation of non-road mobile machinery emission inventories. Based on the remote monitoring and management system of non-road mobile machinery, the actual value of the activity level of non-road mobile machinery was obtained, and the emission inventory of non-road mobile machinery in Shandong Province was established. The emission levels of PM, HC, NOx, and CO from main non-road mobile machinery, including forklifts, excavators, loaders, off-road trucks, and road rollers, were measured. The findings indicate that the operational activity levels of non-road mobile machinery in Shandong Province typically exceeded the guideline’s recommended values. Among them, the annual use time of port terminal ground handling equipment was the longest, with an average annual working time of 4321.5 h per equipment, more than six times the recommended value. Among all types of non-road mobile machinery, loader emissions accounted for the highest proportion, reaching 43.13% of the total emissions of various pollutants. With the tightening of the national standard for non-road mobile machinery from Stage II to Stage III, a significant reduction in actual mechanical emissions was observed, primarily manifested as a 91% decrease in NOx emissions. Based on the data from the remote monitoring platform, a new method for compiling the emission inventory of non-road mobile machinery is proposed in this paper. The calculated emission inventory can reflect more real emission situations and provide a reference and basis for emission control and sustainable emission reduction policy measures for non-road mobile machinery.

show abstract

“…Heavy construction and mining equipment also has huge impact on the environmental e.g. air and water pollution (Jung et al, 2009;Li et al, 2023;Waluś et al, 2018). As society and science and technology advance, the problem of energy scarcity and severe environmental contamination has gained more attention (Liu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Model for Predicting Excavator Downtime

Brkić,

Mihajlović,

Perišić

et al. 2024

Proceedings of the 21st International Conference on Smart Business Technologies

0

View full text Add to dashboard Cite

Previous research shows the significance of maintenance in enhancing performance levels and reducing system costs of equipment. This paper aims to develop a quantitative model for predicting the failure rate of excavators using artificial neural networks (ANN). As an input to the ANN, the duration times of 590 excavator downtimes measured over 198 days at the mining site in Serbia were used to obtain a classification of failures longer than an hour based on the previous 14 days, in aim to prevent potential indirect financial losses which could be over 15000€/hour. A Pareto analysis of the observed data was also performed and showed the technological type of downtime as the most frequent. The results show that the ANN modeling is suitable for mapping the non-linear relationship between excavation activities and the failure rates of excavators. The results showed that the proposed ANN model provides an accurate estimating tool for the early planning stage to predict failure rates of excavators. Future research avenue proposal is directed at monitoring and forecasting the exact duration of excavator downtime in real-time.

show abstract

“…Heavy construction and mining equipment also has huge impact on the environmental e.g. air and water pollution (Jung et al, 2009;Li et al, 2023;Waluś et al, 2018). As society and science and technology advance, the problem of energy scarcity and severe environmental contamination has gained more attention (Liu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the 21st International Conference on Smart Business Technologies

2024

0

View full text Add to dashboard Cite

Previous research shows the significance of maintenance in enhancing performance levels and reducing system costs of equipment. This paper aims to develop a quantitative model for predicting the failure rate of excavators using artificial neural networks (ANN). As an input to the ANN, the duration times of 590 excavator downtimes measured over 198 days at the mining site in Serbia were used to obtain a classification of failures longer than an hour based on the previous 14 days, in aim to prevent potential indirect financial losses which could be over 15000€/hour. A Pareto analysis of the observed data was also performed and showed the technological type of downtime as the most frequent. The results show that the ANN modeling is suitable for mapping the non-linear relationship between excavation activities and the failure rates of excavators. The results showed that the proposed ANN model provides an accurate estimating tool for the early planning stage to predict failure rates of excavators. Future research avenue proposal is directed at monitoring and forecasting the exact duration of excavator downtime in real-time.

show abstract

Emissions of air pollutants from non-road construction machinery in Beijing from 2015 to 2019

Cited by 14 publications

References 29 publications

Investigation of Emission Inventory for Non-Road Mobile Machinery in Shandong Province: An Analysis Grounded in Real-World Activity Levels

Investigation of Emission Inventory for Non-Road Mobile Machinery in Shandong Province: An Analysis Grounded in Real-World Activity Levels

Artificial Neural Network Model for Predicting Excavator Downtime

Proceedings of the 21st International Conference on Smart Business Technologies

Contact Info

Product

Resources

About