PMID- 36842381
OWN - NLM
STAT- MEDLINE
DCOM- 20230321
LR  - 20230322
IS  - 1873-6750 (Electronic)
IS  - 0160-4120 (Linking)
VI  - 173
DP  - 2023 Mar
TI  - Multi-output machine learning model for regional air pollution forecasting in Ho 
      Chi Minh City, Vietnam.
PG  - 107848
LID - S0160-4120(23)00121-6 [pii]
LID - 10.1016/j.envint.2023.107848 [doi]
AB  - Air pollution concentrations in Ho Chi Minh City (HCMC) have been found to 
      surpass the WHO standard, which has become a very serious problem affecting human 
      health and the ecosystem. Various machine learning algorithms have recently been 
      widely used in air quality forecasting studies to predict possible impacts. 
      Training and constructing several machine learning models for different air 
      pollutants, such as NO(2), SO(2), O(3), and CO forecasts, is a time-consuming 
      process that necessitates additional effort for deployment, maintenance, and 
      monitoring. In this paper, an effort has been made to develop a multi-step 
      multi-output multivariate model (a global model) for air quality forecasting, 
      taking into account various parameters such as meteorological conditions, air 
      quality data from urban traffic, residential, and industrial areas, urban space 
      information, and time component for the prediction of NO(2), SO(2), O(3), CO 
      hourly (1 h to 24 h) concentrations. The global forecasting model can anticipate 
      multiple air pollutant concentrations concurrently, based on past concentrations 
      of covariate characteristics. The datasets on air pollution time series were 
      gathered from six HealthyAir air quality monitoring sites in HCMC between 
      February 2021 and August 2022. Darksky weather provided the hourly concentrations 
      of meteorological conditions for the same period. This is the first model built 
      using real-time air quality data for NO(2), SO(2), CO, and O(3) forecasting in 
      HCM city. To assess the effectiveness of the proposed model, it was evaluated 
      using real data from HealthyAir stations and quantified using Root Mean Squared 
      Error (RMSE), Mean Absolute Percentage Error (MAPE), and correlation indices. The 
      results show that the global air quality forecasting model beats earlier models 
      built for air quality forecasting of each specific pollutant in HCMC.
CI  - Copyright (c) 2023. Published by Elsevier Ltd.
FAU - Rakholia, Rajnish
AU  - Rakholia R
AD  - Ireland's National Centre for Applied Artificial Intelligence (CeADAR), 
      University College Dublin, NexusUCD, Belfield Office Park, Dublin, Ireland. 
      Electronic address: rajnish.rakholia@ucd.ie.
FAU - Le, Quan
AU  - Le Q
AD  - Ireland's National Centre for Applied Artificial Intelligence (CeADAR), 
      University College Dublin, NexusUCD, Belfield Office Park, Dublin, Ireland.
FAU - Quoc Ho, Bang
AU  - Quoc Ho B
AD  - Institute for Environment and Resources (IER), Ho Chi Minh City 700000, Vietnam; 
      Department of Science and Technology, Vietnam National University, Ho Chi Minh 
      City 700000, Vietnam.
FAU - Vu, Khue
AU  - Vu K
AD  - Institute for Environment and Resources (IER), Ho Chi Minh City 700000, Vietnam.
FAU - Simon Carbajo, Ricardo
AU  - Simon Carbajo R
AD  - Ireland's National Centre for Applied Artificial Intelligence (CeADAR), 
      University College Dublin, NexusUCD, Belfield Office Park, Dublin, Ireland.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230223
PL  - Netherlands
TA  - Environ Int
JT  - Environment international
JID - 7807270
RN  - S7G510RUBH (Nitrogen Dioxide)
RN  - 0 (Air Pollutants)
RN  - 0 (Particulate Matter)
SB  - IM
MH  - Humans
MH  - Nitrogen Dioxide
MH  - Vietnam
MH  - Ecosystem
MH  - *Air Pollution/analysis
MH  - *Air Pollutants/analysis
MH  - Environmental Monitoring/methods
MH  - Forecasting
MH  - Particulate Matter/analysis
OTO - NOTNLM
OT  - Air quality forecasting
OT  - CO
OT  - HO Chi Minh City
OT  - Multi-output machine learning model
OT  - N-BEATS
OT  - NO(2)
OT  - O(3)
OT  - SO(2)
OT  - Vietnam
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/02/27 06:00
MHDA- 2023/03/22 06:00
CRDT- 2023/02/26 18:32
PHST- 2022/12/05 00:00 [received]
PHST- 2023/01/31 00:00 [revised]
PHST- 2023/02/21 00:00 [accepted]
PHST- 2023/02/27 06:00 [pubmed]
PHST- 2023/03/22 06:00 [medline]
PHST- 2023/02/26 18:32 [entrez]
AID - S0160-4120(23)00121-6 [pii]
AID - 10.1016/j.envint.2023.107848 [doi]
PST - ppublish
SO  - Environ Int. 2023 Mar;173:107848. doi: 10.1016/j.envint.2023.107848. Epub 2023 
      Feb 23.