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

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

doi:10.1016/j.chemosphere.2022.137636

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

Chemosphere. 2023 Feb:313:137636. doi: 10.1016/j.chemosphere.2022.137636. Epub 2022 Dec 22.

Authors

Khalid Elbaz¹, Ibrahim Hoteit², Wafaa Mohamed Shaban³, Shui-Long Shen⁴

Affiliations

¹ MOE Key Laboratory of Intelligent Manufacturing Technology, Department of Civil and Environmental Engineering, College of Engineering, Shantou University, Shantou, Guangdong, 515063, China. Electronic address: khalid@stu.edu.cn.
² Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
³ MOE Key Laboratory of Intelligent Manufacturing Technology, Department of Civil and Environmental Engineering, College of Engineering, Shantou University, Shantou, Guangdong, 515063, China; Department of Civil Engineering, Misr Higher Institute of Engineering and Technology, Mansoura, Egypt.
⁴ MOE Key Laboratory of Intelligent Manufacturing Technology, Department of Civil and Environmental Engineering, College of Engineering, Shantou University, Shantou, Guangdong, 515063, China. Electronic address: Shensl@stu.edu.cn.

PMID: 36566787
DOI: 10.1016/j.chemosphere.2022.137636

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 PM_2.5 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 PM_2.5 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.

Keywords: Air quality; Chemical constituents; Deep learning; NEOM City; Spatiotemporal features.

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
Environmental Monitoring / methods
Forecasting
Particulate Matter / analysis
Risk Assessment

Substances

Air Pollutants
Particulate Matter