AI-based air quality PM2.5 forecasting models for developing countries: A case study of Ho Chi Minh City, Vietnam

被引:19
作者
Rakholia, Rajnish [1 ]
Le, Quan [1 ]
Vu, Khue [2 ]
Ho, Bang Quoc [2 ,3 ,4 ]
Carbajo, Ricardo Simon [1 ]
机构
[1] Univ Coll Dublin, Irelands Natl Ctr Appl Artificial Intelligence CeA, Nexus UCD, Belfield Off Pk, Dublin, Ireland
[2] Inst Environm & Resources IER, Ho Chi Minh City 700000, Vietnam
[3] Vietnam Natl Univ, Dept Sci & Technol, Ho Chi Minh City 700000, Vietnam
[4] Inst Environm & Resources IER, 142 Hien Thanh St,Dist 10, Ho Chi Minh City 700000, Vietnam
关键词
PM2; 5; forecasting; Air quality prediction; Spatiotemporal analysis; Machine learning; Ho Chi Minh City; Vietnam;
D O I
10.1016/j.uclim.2022.101315
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Outdoor air pollution damages the climate and causes many diseases, including cardiovascular diseases, respiratory infections, and lung damage. In particular, Particulate Matter (PM2.5) is considered a hazardous air pollutant to human health. Accurate hourly forecasting of PM2.5 concentrations is thus of significant importance for public health, helping the citizens to plan the measures to alleviate the harmful effects of air pollution on health. This study analyses and discusses the temporal characteristics of PM2.5 at different locations in Ho Chi Minh City (HCMC), Vietnam -an economic center and a megacity in a developing country with a population of 8.99 million people. We developed several AI-based one-shot multi-step PM2.5 forecasting models, with both an hourly forecast granularity (1 h to 24 h) and a 24-h rolling mean. These Machine Learning algorithms include Stochastic Gradient Descent Regres-sor, hybrid 1D CNN-LSTM, eXtreme Gradient Boosting Regressor, and Prophet. We collected the data from six monitoring stations installed by the HealthyAir project partners at different loca-tions in HCMC, including traffic, residential and industrial areas in the city. In addition, we developed a suitable model training protocol using data from a short period to address the non-stationarity of PM2.5 time series. Our proposed PM2.5 forecasting models achieve state-of-the-art accuracy and will be deployed in our HealthyAir mobile app to warn HCMC citizens of air pollution issues in the city.
引用
收藏
页数:13
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