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Impact of Green Energy Transportation Systems on Urban Air Quality: A Predictive Analysis Using Spatiotemporal Deep Learning Techniques

Author

Listed:
  • Rafia Mumtaz

    (School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Arslan Amin

    (School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Muhammad Ajmal Khan

    (School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Muhammad Daud Abdullah Asif

    (School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Zahid Anwar

    (Department of Computer Science, The Sheila and Robert Challey Institute for Global Innovation and Growth, North Dakota State University (NDSU), Fargo, ND 58102, USA)

  • Muhammad Jawad Bashir

    (School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

Abstract

Transitioning to green energy transport systems, notably electric vehicles, is crucial to both combat climate change and enhance urban air quality in developing nations. Urban air quality is pivotal, given its impact on health, necessitating accurate pollutant forecasting and emission reduction strategies to ensure overall well-being. This study forecasts the influence of green energy transport systems on the air quality in Lahore and Islamabad, Pakistan, while noting the projected surge in electric vehicle adoption from less than 1% to 10% within three years. Predicting the impact of this change involves analyzing data before, during, and after the COVID-19 pandemic. The lockdown led to minimal fossil fuel vehicle usage, resembling a green energy transportation scenario. The novelty of this work is twofold. Firstly, remote sensing data from the Sentinel-5P satellite were utilized to predict air quality index (AQI) trends before, during, and after COVID-19. Secondly, deep learning models, including long short-term memory (LSTM) and bidirectional LSTM, and machine learning models, including decision tree and random forest regression, were utilized to forecast the levels of NO 2 , SO 2 , and CO in the atmosphere. Our results demonstrate that implementing green energy transportation systems in urban centers of developing countries can enhance air quality by approximately 98%. Notably, the bidirectional LSTM model outperformed others in predicting NO 2 and SO 2 concentrations, while the LSTM model excelled in forecasting CO concentration. These results offer valuable insights into predicting air pollution levels and guiding green energy policies to mitigate the adverse health effects of air pollution.

Suggested Citation

  • Rafia Mumtaz & Arslan Amin & Muhammad Ajmal Khan & Muhammad Daud Abdullah Asif & Zahid Anwar & Muhammad Jawad Bashir, 2023. "Impact of Green Energy Transportation Systems on Urban Air Quality: A Predictive Analysis Using Spatiotemporal Deep Learning Techniques," Energies, MDPI, vol. 16(16), pages 1-30, August.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:6087-:d:1221619
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    References listed on IDEAS

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    1. Noor ul Ain Binte Wasif Ali & Sarah Amir & Kanwar Muhammad Javed Iqbal & Ashfaq Ahmad Shah & Zafeer Saqib & Nadia Akhtar & Wahid Ullah & Muhammad Atiq Ur Rehman Tariq, 2022. "Analysis of Land Surface Temperature Dynamics in Islamabad by Using MODIS Remote Sensing Data," Sustainability, MDPI, vol. 14(16), pages 1-15, August.
    2. Vasco Simões & Leandro Pereira & Álvaro Dias, 2023. "Enhancing Sustainable Business Models for Green Transportation," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    3. Katerina Vichova & Petr Veselik & Romana Heinzova & Radek Dvoracek, 2021. "Road Transport and Its Impact on Air Pollution during the COVID-19 Pandemic," Sustainability, MDPI, vol. 13(21), pages 1-15, October.
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    Cited by:

    1. João Reis, 2023. "Exploring Applications and Practical Examples by Streamlining Material Requirements Planning (MRP) with Python," Logistics, MDPI, vol. 7(4), pages 1-19, December.

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