大气与环境光学学报, 2022, 17 (2): 220, 网络出版: 2022-07-22   

移动源排放遥测主要影响因素分析及预测

Analysis and prediction of main influencing factors in mobile source remote sensing
许镇义 1,2,*王瑞宾 1,3康宇 1,2,4,5曹洋 2张聪 6王仁军 1,3
作者单位
1 合肥综合性国家科学中心人工智能研究院, 安徽 合肥 230088
2 中国科学技术大学自动化系, 安徽 合肥 230036
3 安徽大学计算机科学与技术学院, 安徽 合肥 230601
4 中国科学技术大学先进技术研究院, 安徽 合肥 230088
5 中国科学技术大学火灾科学国家重点实验室, 安徽 合肥 230027
6 合肥市生态环境局, 安徽 合肥 230601
摘要
由于移动源污染遥感监测受到复杂外部环境影响, 难以通过传统统计方法建立车辆行驶工况与污染排放之间的相关性模型, 为此开展了基于移动源遥感监测的影响因素分析及排放预测的研究。首先利用 Spearman 相关性分析排除与移动源污染物主要排放气体 CO、HC、NO 气体浓度无相关性的因素; 其次使用 Lasso 算法确定各成分的关键影响因子, 并采用神经网络构建污染物排放预测模型; 最后在测试集上验证该模型用于移动源污染排放主要成分预测的有效性。模型预测的结果表明, 基于特征筛选的移动源污染排放数据预测神经网络模型具有较高的预测精度, 可以降低城市移动源污染排放检测成本并为相关部门制定相关政策提供数据支持。
Abstract
As remote sensing monitoring of mobile source exhaust can be affected by the complex external environment, it is difficult to establish a correaltion model between vehicle driving conditions and pollution emissions through traditional statistical methods. For this reason, the research on the analysis of influencing factors and emission prediction based on remote sensing monitoring of mobile sources is carried out. Firstly, Spearman correlation is usedto exclude the factors that have no correlation with CO, HC and NO, the main components in emission of mobile source pollution. Secondly, Lasso algorithm is used to choose the principal influencing factors. And after principal components analysis and theselection of algorithm and architecture, the Back-Propagation (BP) neural network model is established as the optimal algorithm. Finally, the validity of the model for predicting the main components of emission of mobile source pollution is verified on the test set. The results of model prediction show that the prediction models based on feature selection and BP has high prediction accuracy, which can reduce the cost of mobile source pollution emission detection and provide theoretical basis for policy making.
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许镇义, 王瑞宾, 康宇, 曹洋, 张聪, 王仁军. 移动源排放遥测主要影响因素分析及预测[J]. 大气与环境光学学报, 2022, 17(2): 220. XU Zhenyi, WANG Ruibin, KANG Yu, CAO Yang, ZHANG Cong, WANG Renjun. Analysis and prediction of main influencing factors in mobile source remote sensing[J]. Journal of Atmospheric and Environmental Optics, 2022, 17(2): 220.

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