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Mobile monitoring and spatial prediction of black carbon in Cairo, Egypt

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Abstract

This study harnesses the power of mobile data in developing a spatial model for predicting black carbon (BC) concentrations within one of the most heavily populated regions in the Middle East and North Africa MENA region, Greater Cairo Region (GCR) in Egypt. A mobile data collection campaign was conducted in GCR to collect BC measurements along specific travel routes. In total, 3,300 km were travelled across a widespread 525 km of routes. Reported average BC values were around 20 µg/m3, announcing an alarming order of magnitude value when compared to the maximum reported values in similar studies. A bi-directional stepwise land use regression (LUR) model was developed to select the best combination of explanatory variables and generate an exposure surface for BC, in addition to a number of machine learning models (random forest gradient boost, light gradient boost model (LightGBM), Keras neural network (NN)). Data from 7 air quality (AQ) stations were compared—in terms of mean square error (MSE) and mean absolute error (MAE)—with predictions from the LUR and the NN model. The NN model estimated higher BC concentrations in the downtown areas, while lower concentrations are estimated for the peripheral area at the east side of the city. Such results shed light on the credibility of the LUR models in generating a general spatial trend of BC concentrations while the superiority of NN in BC accuracy estimation (0.023 vs 0.241 in terms of MSE and 0.12 vs 0.389 in terms of MAE; of NN vs LUR respectively).

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health

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Acknowledgements

The authors acknowledge the support of the IDRC (International Development Research Centre), particularly Dr. Raed Sharif (Technology and Innovation Officer in the MENA Region). The authors would also like to deeply acknowledge the tireless efforts of the research team from the Access to Knowledge for Development (A2k4D) Centre, at the American University of Cairo for their valuable feedback and comments throughout the development of the project, led by Prof. Nagle Rizk (Professor of Economics) and Ms. Nancy Salem (Senior Research Officer). Additionally, the data collection team from SETS North Africa, led by Eng. Ahmed Adham, and the modelling efforts of Engr. Abdelrahman Samaha shall be acknowledged.

Funding

The research was supported by the Canadian International Development Research Centre (IDRC).

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Authors and Affiliations

  1. Faculty of Engineering, Cairo University, Giza, 12631, Egypt

    Hoda Talaat & Hossam Abdelgawad

  2. Department of Civil & Mineral Engineering, University of Toronto, Toronto, Canada

    Junshi Xu & Marianne Hatzopoulou

  3. Urban Transport Technologies, SETS International, Beirut, 113-7742, Lebanon

    Hossam Abdelgawad

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  1. Hoda Talaat
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  2. Junshi Xu
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  3. Marianne Hatzopoulou
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  4. Hossam Abdelgawad
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Correspondence to Hossam Abdelgawad.

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Talaat, H., Xu, J., Hatzopoulou, M. et al. Mobile monitoring and spatial prediction of black carbon in Cairo, Egypt. Environ Monit Assess 193, 587 (2021). https://doi.org/10.1007/s10661-021-09351-0

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