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Outdoor air quality data for spatiotemporal analysis and air quality modelling in Ho Chi Minh City, Vietnam: A part of HealthyAir Project.
Rakholia, Rajnish; Le, Quan; Vu, Khue Hoang Ngoc; Ho, Bang Quoc; Carbajo, Ricardo Simon.
Afiliação
  • Rakholia R; Ireland's National Centre for Applied Artificial Intelligence (CeADAR), University College Dublin, NexusUCD, Belfield Office Park, Dublin, Ireland.
  • Le Q; Ireland's National Centre for Applied Artificial Intelligence (CeADAR), University College Dublin, NexusUCD, Belfield Office Park, Dublin, Ireland.
  • Vu KHN; Institute for Environment and Resources (IER), Ho Chi Minh City 700000, Vietnam; 142 To Hien Thanh St.,Dist.10., HCMC.
  • Ho BQ; Institute for Environment and Resources (IER), Ho Chi Minh City 700000, Vietnam; 142 To Hien Thanh St.,Dist.10., HCMC.
  • Carbajo RS; Department of Academic Affairs, Vietnam National University, Ho Chi Minh City; Community 6, Linh Trung Ward, HCMC, 700000, Vietnam.
Data Brief ; 46: 108774, 2023 Feb.
Article em En | MEDLINE | ID: mdl-36478689
This article presents outdoor air pollution data acquired from the real-time Air Quality Monitoring Network (AQMN), which was established by the Healthyair project team in Ho Chi Minh City (HCMC), Vietnam. The AQMN is made up of six air pollution monitoring stations spread over the city (Traffic, Residential, and Industrial). Each station measures the same contaminants in the air, including PM2.5, TSP, NO2, SO2, O3, CO, and two meteorological factors, temperature and humidity. This data is crucial for air quality modelling, spatiotemporal analysis, correlation analysis, and assessing local air pollution around the city. The data was first obtained in minute frequency, then transformed and produced in hourly frequency for analysis and modelling. The PM2.5 data from this dataset was used to construct an hourly air quality PM2.5 forecasting model in the publication titled "AI-based Air Quality PM2.5 Forecasting Models for Developing Countries: A Case Study of Ho Chi Minh City, Vietnam" by Rakholia et. al. (2022).
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article