Prediction of on-road CO2 emissions with high spatio-temporal resolution implementing multilayer perceptron

IF 3.4 Q2 ENVIRONMENTAL SCIENCES
Hao Yang , Kuang Xiao , Xing Xiang , Xing Wang , Xi Wang , Yunsong Du , Guangming Shi , Xin Zheng , Hongli Tao , Huanbo Wang , Fumo Yang
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引用次数: 0

Abstract

On-road carbon emissions represent a significant portion of transportation emissions in China and are a critical focus for future carbon reduction efforts. High spatio-temporal resolution emission inventories are vital for facilitating dynamic carbon reduction in cities. This study employs the Multilayer Perceptron (MLP) model to simulate variations in road traffic volume at the segment level and predict on-road CO2 emissions with high spatio-temporal resolution. The results demonstrate that this method can effectively reproduce the spatio-temporal distribution of on-road traffic, with R2 exceeding 0.6 for most road types and overall RMSE of 88 vehicles/h, respectively. Applied in Chengdu's Jinniu District, southwestern China, results show CO2 emissions peak during morning (7–9 a.m.) and evening (16–18 p.m.) commutes, concentrated on main roads. Morning peaks are lower but grow faster than evening peaks. CO2 emissions significantly increase on holidays and weekends with moderate temperatures and no or light rain. These insights support urban dynamic carbon reduction planning.
基于多层感知器的高时空分辨率道路CO2排放预测
道路碳排放占中国交通排放的很大一部分,是未来碳减排工作的重点。高时空分辨率排放清单对于促进城市动态碳减排至关重要。本研究采用多层感知器(Multilayer Perceptron, MLP)模型在路段水平上模拟道路交通量的变化,并以高时空分辨率预测道路上的二氧化碳排放。结果表明,该方法能有效再现道路交通时空分布,大部分道路类型R2均超过0.6,总体RMSE为88辆/h。在中国西南部成都市金牛区应用的结果显示,二氧化碳排放量在早上7点到9点和晚上16点到18点的通勤时间达到峰值,主要集中在主要道路上。早晨的峰值较低,但增长速度快于晚上的峰值。在气温适中、无雨或小雨的假期和周末,二氧化碳排放量显著增加。这些见解支持城市动态碳减排规划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Atmospheric Environment: X
Atmospheric Environment: X Environmental Science-Environmental Science (all)
CiteScore
8.00
自引率
0.00%
发文量
47
审稿时长
12 weeks
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