摘要
为保证公路隧道内洁净安全,需对其运营环境进行通风设计。现行公路隧道运营通风污染物基准排放量年折减率以传统化石能源车辆排放为标准,而“双碳”目标下随着新能源技术的发展和革新,实际移动源污染物排放量年折减率远低于原规范折减率。为降低实际隧道运营中通风成本的投入,探究新技术下公路隧道通风年折减率的合理取值,考虑汽车保有量、污染物排放量和公路隧道总里程等因素,采用移动平均和神经网络的方法对基准排放量年折减率进行预测和修正。经研究,建议一氧化碳基准排放量年折减率修正为2.3%,颗粒物基准排放量年折减率修正为2.4%,实现绿色、环保、节能的经济目标。
To ensure cleanliness and safety in road tunnels,ventilation design is required for their operating environment.The current annual reduction rate of the baseline pollutant emissions from road tunnel operation is based on the emissions of vehicles driven by traditional fossil energy sources,while the actual annual reduction rate of pollutant emissions from mobile sources is much lower than the original specification due to the development and innovation of new energy technologies in China,which is under the double carbon target.In order to reduce the cost of ventilation in actual tunnel operations and to investigate the reasonable value of the annual reduction rate of road tunnel ventilation under the new technology,a moving average and neural network approach was used to predict and correct the annual reduction rate of baseline emissions considering vehicle ownership,pollutant emissions and total road tunnel mileage.It is suggested that the annual reduction rate of carbon monoxide emissions should be revised to 2.3%and the annual reduction rate of particulate matter emissions should be revised to 2.4%to achieve the economic goal of green,environmental protection and energy saving.
作者
许昱旻
郭春
XU Yumin;GUO Chun(School of Civil Engineering,Southwest JiaoTong University,Chengdu 610031;MOE Key Laboratory of Transportation Tunnel Engineering,Southwest JiaoTong University,Chengdu 610031)
出处
《现代隧道技术》
CSCD
北大核心
2022年第S01期121-127,共7页
Modern Tunnelling Technology
基金
“十三五”国家重点研发计划(2019YFC0605104)
四川省社会科学规划项目(SC22B031)
四川省教育科研资助金项目(SCJG20A120)
关键词
公路隧道
运营通风
神经网络
移动平均
预测修正
Road tunnels
Operational ventilation
Neural networks
Moving average
Prediction and correction
