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

被引:23
作者
Elbaz, Khalid [1 ]
Hoteit, Ibrahim [2 ]
Shaban, Wafaa Mohamed [1 ,3 ]
Shen, Shui-Long [1 ]
机构
[1] Shantou Univ, Coll Engn, Dept Civil & Environm Engn, MOE Key Lab Intelligent Mfg Technol, Shantou 515063, Guangdong, Peoples R China
[2] King Abdullah Univ Sci & Technol KAUST, Phys Sci & Engn Div, Thuwal, Saudi Arabia
[3] Misr Higher Inst Engn & Technol, Dept Civil Engn, Mansoura, Egypt
关键词
NEOM City; Air quality; Deep learning; Spatiotemporal features; Chemical constituents; PREDICTION; POLLUTION;
D O I
10.1016/j.chemosphere.2022.137636
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Modeling and predicting air pollution concentrations is important to provide early warnings about harmful at-mospheric 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.
引用
收藏
页数:10
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