环境科学
温室气体
高度(三角形)
碳纤维
运输工程
环境工程
工程类
计算机科学
生态学
几何学
数学
算法
复合数
生物
作者
Zhiwen Jiang,Lin Wu,Hong Niu,Zhenyu Jia,Zhaoyu Qi,Yan Liu,Qi‐Jun Zhang,Ting Wang,Jianfei Peng,Hongjun Mao
标识
DOI:10.1016/j.scitotenv.2024.170671
摘要
This study addresses the literature gap concerning accurately identifying vehicle carbon emission characteristics in high-altitude areas. Utilizing a portable emission measurement system (PEMS) for real-world testing, we quantified the influence of altitude on carbon emissions from light-duty gasoline (LDGV) and diesel vehicles (LDDV). The Random Forest (RF) algorithm was employed to analyze the complex nonlinear relationships between altitude, meteorological conditions, driving patterns, and carbon dioxide (CO2) emissions, enabling predictions across different altitudes. The results showed that CO2 emissions progressively increase with elevation. Furthermore, as altitude increases, combustion efficiency declines, and the overall impact of driving conditions on emission rates diminishes. Altitude and meteorological factors significantly contributed to CO2 emissions, whereas driving conditions and road grades contributed less. Compared with the COPERT model, the RF model demonstrates strong accuracy in predicting carbon emissions at different altitudes. Specifically, the CO2 emission rate nearly triples as altitude increases from 2.0 km to 4.5 km. This research bridges a critical gap in the understanding carbon emissions from high-altitude vehicles, offering insights into policy development for emission reduction strategies in such regions. Future studies should integrate diverse testing methodologies and comprehensive surveys to validate and extend the findings.
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