Spatiotemporal air quality forecasting and health risk assessment over smart city of NEOM.

Khalid Elbaz, Ibrahim Hoteit, Wafaa Mohamed Shaban, Shui-Long Shen

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Modeling and predicting air pollution concentrations is important to provide early warnings about harmful atmospheric substances. However, uncertainty in the dynamic process and limited information about chemical constituents and emissions sources make air-quality predictions very difficult. This study proposed a novel deep-learning method to extract high levels of abstraction in data and capture spatiotemporal features at hourly and daily time intervals in NEOM City, Saudi Arabia. The proposed method integrated a residual network (ResNet) with the convolutional long short-term memory (ConvLSTM). The ConvLSTM method was boosted by a ResNet model for deeply extracting the spatial features from meteorological and pollutant data and thereby mitigating the loss of feature information. Then, health risk assessment was put forward to evaluate PM10 and PM2.5 risk sensitivity in five districts in NEOM City. Results revealed that the proposed method with effective feature extraction could greatly optimize the accuracy of spatiotemporal air quality forecasts compared to existing state-of-the-art models. For the next hour prediction tasks, the PM10 and PM2.5 of MASE were 9.13 and 13.57, respectively. The proposed method provides an effective solution to improve the prediction of air-pollution concentrations while being portable to other regions around the world.
Original languageEnglish (US)
Pages (from-to)137636
JournalChemosphere
Volume313
DOIs
StatePublished - Dec 28 2022

Bibliographical note

KAUST Repository Item: Exported on 2023-01-02
Acknowledgements: The research work was funded by the Research Fund for International Scientists of National Natural Science Fund of China (NSFC: Grant No. 5221101449) and the Scientific Research Initiation Grant of Shantou University for New Faculty Member (Grant No. NTF22033).

ASJC Scopus subject areas

  • Environmental Chemistry
  • General Chemistry

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