Towards Cleaner Cities: Estimating Vehicle-Induced PM<sub>2.5</sub> with Hybrid EBM-CMA-ES Modeling
In developing countries, vehicle emissions are a major source of atmospheric pollution, worsened by aging vehicle fleets and less stringent emissions regulations. This results in elevated levels of particulate matter, contributing to the degradation of urban air quality and increasing concerns over...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Toxics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2305-6304/12/11/827 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850267098519961600 |
|---|---|
| author | Saleh Alotaibi Hamad Almujibah Khalaf Alla Adam Mohamed Adil A. M. Elhassan Badr T. Alsulami Abdullah Alsaluli Afaq Khattak |
| author_facet | Saleh Alotaibi Hamad Almujibah Khalaf Alla Adam Mohamed Adil A. M. Elhassan Badr T. Alsulami Abdullah Alsaluli Afaq Khattak |
| author_sort | Saleh Alotaibi |
| collection | DOAJ |
| description | In developing countries, vehicle emissions are a major source of atmospheric pollution, worsened by aging vehicle fleets and less stringent emissions regulations. This results in elevated levels of particulate matter, contributing to the degradation of urban air quality and increasing concerns over the broader effects of atmospheric emissions on human health. This study proposes a Hybrid Explainable Boosting Machine (EBM) framework, optimized using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), to predict vehicle-related PM<sub>2.5</sub> concentrations and analyze contributing factors. Air quality data were collected from Open-Seneca sensors installed along the Nairobi Expressway, alongside meteorological and traffic data. The CMA-ES-tuned EBM model achieved a Mean Absolute Error (MAE) of 2.033 and an R<sup>2</sup> of 0.843, outperforming other models. A key strength of the EBM is its interpretability, revealing that the location was the most critical factor influencing PM<sub>2.5</sub> concentrations, followed by humidity and temperature. Elevated PM<sub>2.5</sub> levels were observed near the Westlands roundabout, and medium to high humidity correlated with higher PM<sub>2.5</sub> levels. Furthermore, the interaction between humidity and traffic volume played a significant role in determining PM<sub>2.5</sub> concentrations. By combining CMA-ES for hyperparameter optimization and EBM for prediction and interpretation, this study provides both high predictive accuracy and valuable insights into the environmental drivers of urban air pollution, providing practical guidance for air quality management. |
| format | Article |
| id | doaj-art-47dae3be98a34fc09823fa359d190d31 |
| institution | OA Journals |
| issn | 2305-6304 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Toxics |
| spelling | doaj-art-47dae3be98a34fc09823fa359d190d312025-08-20T01:53:56ZengMDPI AGToxics2305-63042024-11-01121182710.3390/toxics12110827Towards Cleaner Cities: Estimating Vehicle-Induced PM<sub>2.5</sub> with Hybrid EBM-CMA-ES ModelingSaleh Alotaibi0Hamad Almujibah1Khalaf Alla Adam Mohamed2Adil A. M. Elhassan3Badr T. Alsulami4Abdullah Alsaluli5Afaq Khattak6Civil and Environmental Engineering Department, Faculty of Engineering—Rabigh Branch, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Civil Engineering, College of Engineering, Taif University, Taif 21944, Saudi ArabiaDepartment of Civil Engineering, College of Engineering, Bisha University, Bisha 61361, Saudi ArabiaDepartment of Civil Engineering, College of Engineering, Taif University, Taif 21944, Saudi ArabiaDepartment of Civil Engineering, College of Engineering and Architecture, Umm Al-Qura University, Makkah 24382, Saudi ArabiaDepartment of Civil Engineering, College of Engineering, Taif University, Taif 21944, Saudi ArabiaDepartment of Civil, Structural and Environmental Engineering, Trinity College Dublin, D02 PN40 Dublin, IrelandIn developing countries, vehicle emissions are a major source of atmospheric pollution, worsened by aging vehicle fleets and less stringent emissions regulations. This results in elevated levels of particulate matter, contributing to the degradation of urban air quality and increasing concerns over the broader effects of atmospheric emissions on human health. This study proposes a Hybrid Explainable Boosting Machine (EBM) framework, optimized using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), to predict vehicle-related PM<sub>2.5</sub> concentrations and analyze contributing factors. Air quality data were collected from Open-Seneca sensors installed along the Nairobi Expressway, alongside meteorological and traffic data. The CMA-ES-tuned EBM model achieved a Mean Absolute Error (MAE) of 2.033 and an R<sup>2</sup> of 0.843, outperforming other models. A key strength of the EBM is its interpretability, revealing that the location was the most critical factor influencing PM<sub>2.5</sub> concentrations, followed by humidity and temperature. Elevated PM<sub>2.5</sub> levels were observed near the Westlands roundabout, and medium to high humidity correlated with higher PM<sub>2.5</sub> levels. Furthermore, the interaction between humidity and traffic volume played a significant role in determining PM<sub>2.5</sub> concentrations. By combining CMA-ES for hyperparameter optimization and EBM for prediction and interpretation, this study provides both high predictive accuracy and valuable insights into the environmental drivers of urban air pollution, providing practical guidance for air quality management.https://www.mdpi.com/2305-6304/12/11/827air qualityPM<sub>2.5</sub>explainable boosting machinecovariance matrix adaptation evolution strategy |
| spellingShingle | Saleh Alotaibi Hamad Almujibah Khalaf Alla Adam Mohamed Adil A. M. Elhassan Badr T. Alsulami Abdullah Alsaluli Afaq Khattak Towards Cleaner Cities: Estimating Vehicle-Induced PM<sub>2.5</sub> with Hybrid EBM-CMA-ES Modeling Toxics air quality PM<sub>2.5</sub> explainable boosting machine covariance matrix adaptation evolution strategy |
| title | Towards Cleaner Cities: Estimating Vehicle-Induced PM<sub>2.5</sub> with Hybrid EBM-CMA-ES Modeling |
| title_full | Towards Cleaner Cities: Estimating Vehicle-Induced PM<sub>2.5</sub> with Hybrid EBM-CMA-ES Modeling |
| title_fullStr | Towards Cleaner Cities: Estimating Vehicle-Induced PM<sub>2.5</sub> with Hybrid EBM-CMA-ES Modeling |
| title_full_unstemmed | Towards Cleaner Cities: Estimating Vehicle-Induced PM<sub>2.5</sub> with Hybrid EBM-CMA-ES Modeling |
| title_short | Towards Cleaner Cities: Estimating Vehicle-Induced PM<sub>2.5</sub> with Hybrid EBM-CMA-ES Modeling |
| title_sort | towards cleaner cities estimating vehicle induced pm sub 2 5 sub with hybrid ebm cma es modeling |
| topic | air quality PM<sub>2.5</sub> explainable boosting machine covariance matrix adaptation evolution strategy |
| url | https://www.mdpi.com/2305-6304/12/11/827 |
| work_keys_str_mv | AT salehalotaibi towardscleanercitiesestimatingvehicleinducedpmsub25subwithhybridebmcmaesmodeling AT hamadalmujibah towardscleanercitiesestimatingvehicleinducedpmsub25subwithhybridebmcmaesmodeling AT khalafallaadammohamed towardscleanercitiesestimatingvehicleinducedpmsub25subwithhybridebmcmaesmodeling AT adilamelhassan towardscleanercitiesestimatingvehicleinducedpmsub25subwithhybridebmcmaesmodeling AT badrtalsulami towardscleanercitiesestimatingvehicleinducedpmsub25subwithhybridebmcmaesmodeling AT abdullahalsaluli towardscleanercitiesestimatingvehicleinducedpmsub25subwithhybridebmcmaesmodeling AT afaqkhattak towardscleanercitiesestimatingvehicleinducedpmsub25subwithhybridebmcmaesmodeling |