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Spatial estimation of outdoor NO2 levels in Central London using deep neural networks and a wavelet decomposition technique

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  • Cabaneros, Sheen Mclean
  • Calautit, John Kaiser
  • Hughes, Ben

Abstract

Outdoor air pollution remains a major environmental threat to the public, especially those who reside in highly urbanised areas. Recent studies have revealed the effectiveness of early-warning mechanisms that enable the public reduce their exposure to air pollutants. This highlights the need for accurate air quality forecasts. However, air quality in many developing and highly urbanised countries remains unmonitored. Hence, a novel spatiotemporal interpolation modelling approach based on a deep learning and wavelet pre-processing technique was proposed in this paper. In more detail, Long Short-term Memory (LSTM) neural networks and Discrete Wavelet Transformation (DWT) were utilised to model the spatial variability of hourly NO2 levels at six urban sites in Central London, the United Kingdom. The models were trained using only the NO2 concentration data from the neighbouring sites. Benchmark models such as plain LSTM and Multilayer Perceptron (MLP) models were also developed to validate the effectiveness of the proposed models. The proposed wavelet-based spatiotemporal models were found to provide superior forecasting results, explaining 77% to 93% of the variability of the actual NO2 concentration levels at most sites. The overall results reveal the very promising potential of the proposed models for the spatiotemporal characterisation of outdoor air pollution.

Suggested Citation

  • Cabaneros, Sheen Mclean & Calautit, John Kaiser & Hughes, Ben, 2020. "Spatial estimation of outdoor NO2 levels in Central London using deep neural networks and a wavelet decomposition technique," Ecological Modelling, Elsevier, vol. 424(C).
  • Handle: RePEc:eee:ecomod:v:424:y:2020:i:c:s0304380020300892
    DOI: 10.1016/j.ecolmodel.2020.109017
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    References listed on IDEAS

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    1. Bing-Chun Liu & Arihant Binaykia & Pei-Chann Chang & Manoj Kumar Tiwari & Cheng-Chin Tsao, 2017. "Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-17, July.
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    Cited by:

    1. Yuan Huang & Junhao Yu & Xiaohong Dai & Zheng Huang & Yuanyuan Li, 2022. "Air-Quality Prediction Based on the EMD–IPSO–LSTM Combination Model," Sustainability, MDPI, vol. 14(9), pages 1-18, April.
    2. Li, Naiqing & Li, Longhao & Zhang, Fan & Jiao, Ticao & Wang, Shuang & Liu, Xuefeng & Wu, Xinghua, 2023. "Research on short-term photovoltaic power prediction based on multi-scale similar days and ESN-KELM dual core prediction model," Energy, Elsevier, vol. 277(C).
    3. Behm, Svenia & Haupt, Harry, 2020. "Predictability of hourly nitrogen dioxide concentration," Ecological Modelling, Elsevier, vol. 428(C).

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