TPE-LCE-SHAP: A Hybrid Framework for Assessing Vehicle-Related PM2.5 Concentrations

被引：0
|
作者：
Almujibah, Hamad [1 ]
Almaliki, Abdulrazak H. [1 ]
Mongina Matara, Caroline [2 ]
Abdallah Mohammed Elhassan, Adil [1 ]
Alla Adam Mohamed, Khalaf [3 ]
Bakri, Mudthir [4 ]
Khattak, Afaq [5 ]
机构：
[1] Taif University, College of Engineering, Department of Civil Engineering, Taif,21944, Saudi Arabia
[2] Technical University of Kenya, Department of Civil and Construction Engineering, Nairobi,00200, Kenya
[3] Bisha University, College of Engineering, Department of Civil Engineering, Bisha,61361, Saudi Arabia
[4] Qassim University, College of Engineering, Department of Civil Engineering, Buraidah,52571, Saudi Arabia
[5] Tongji University, College of Transportation Engineering, Jiading, Shanghai,201804, China
来源：
IEEE Access | 2024年 / 12卷
关键词：
This study proposes a novel hybrid approach for estimating and analyzing vehicle-related PM2.5 concentrations. The framework integrates the Local Cascade Ensemble (LCE) model; optimized using the Tree-structured Parzen Estimator (TPE) strategy; with SHapley Additive exPlanations (SHAP) to enhance interpretability. It utilizes datasets comprising air quality; meteorological; and traffic data collected from strategically placed sensors along the Nairobi Expressway. Key parameters include hourly traffic volume; average vehicle speed; humidity; wind speed; and temperature. The TPE-tuned LCE model outperformed benchmark algorithms including Random Forest (RF); Extreme Gradient Boosting (XGBoost); Light Gradient Boosting Machine (LightGBM); Adaptive Boosting (AdaBoost); and Multiple Linear Regression (MLR) achieved the lowest Mean Absolute Error (MAE) of 1.94; Mean Squared Error (MSE) of 21.50; Root Mean Squared Error (RMSE) of 4.64; Residual Standard Ratio (RSR) of 0.38; and the highest Coefficient of Determination (R2) of 0.87. SHAP analysis of TPE-tuned LCE model identified location; and wind speed as the most influential predictors of PM2.5 levels. This hybrid framework delivers robust predictive accuracy and actionable insights; making it a valuable tool for effective environmental management and policy making. © 2013 IEEE;
D O I：
10.1109/ACCESS.2024.3505116
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：179219 / 179234
相关论文
共 50 条
  • [31] A system based approach to develop hybrid model predicting extreme urban NOx and PM2.5 concentrations
    Gulia, Sunil
    Nagendra, S. M. Shiva
    Khare, Mukesh
    TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2017, 56 : 141 - 154
  • [32] Uncertainty quantification of PM2.5 concentrations using a hybrid model based on characteristic decomposition and fuzzy granulation
    Zhang, Linyue
    Wang, Jianzhou
    Li, Zhiwu
    Zeng, Bo
    Huang, Xiaojia
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2022, 324
  • [33] ANN-based PM prediction model for assessing the temporal variability of PM10, PM2.5 and PM1 concentrations at an urban roadway
    Srimuruganandam, B.
    Nagendra, S.M. Shiva
    International Journal of Environmental Engineering, 2015, 7 (01) : 60 - 89
  • [34] Estimating exposure to traffic-related PM2.5 for women commuters using vehicle and personal monitoring
    Krall, Jenna R.
    Adibah, Nada
    Babin, Leah M.
    Lee, Yi-Ching
    Motti, Vivian Genaro
    McCombs, Michelle
    McWilliams, Andrea
    Thornburg, Jonathan
    Pollack, Anna Z.
    ENVIRONMENTAL RESEARCH, 2020, 187
  • [35] Traffic volume and road network structure: Revealing transportation-related factors on PM2.5 concentrations
    Yu, Chengcheng
    Deng, Yongqi
    Qin, Zhengtao
    Yang, Chao
    Yuan, Quan
    TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2023, 124
  • [36] Quantifying vehicle restriction related PM2.5 reduction using field observations in an isolated urban basin
    Guo, Yumin
    Tian, Pengfei
    Li, Mengqi
    Yu, Zeren
    Song, Xin
    Shi, Jinsen
    Chang, Yi
    Zhang, Lei
    ENVIRONMENTAL RESEARCH LETTERS, 2024, 19 (02)
  • [37] Mercury levels in road dust and household TSP/PM2.5 related to concentrations in hair in Guangzhou, China
    Huang, Minjuan
    Wang, Wei
    Leung, Homan
    Chan, Chuen Yu
    Liu, Wing Keung
    Wong, Ming Hung
    Cheung, Kwai Chung
    ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY, 2012, 81 : 27 - 35
  • [38] Predicting real-time traffic-related PM10 and PM2.5 emissions and concentrations in urban areas
    Reynolds, A.W.
    Broderick, B.M.
    Journal of Aerosol Science, 2000, 31 (SUPPL. 1)
  • [39] Hybrid Spatio-temporal Deep Learning Framework for Particulate Matter(PM2.5) Concentration Forecasting
    Abirami, S.
    Chitra, P.
    Madhumitha, R.
    Kesavan, Ragul S.
    2020 INTERNATIONAL CONFERENCE ON INNOVATIVE TRENDS IN INFORMATION TECHNOLOGY (ICITIIT), 2020,
  • [40] Predicting real-time traffic-related PM10 and PM2.5 emissions and concentrations in urban areas
    Reynolds, A.W., 1600, Elsevier Ltd (31):
12345